Descriptive findings from a pilot survey in an Algerian STEM context
Bayane Guellal1
The use of Artificial intelligence (AI) has recently predominated different dimensions of higher education. In the context of English-medium instruction (EMI) specifically, both students and teachers seek AI to maneuver content and language simultaneously, especially in STEM settings that require both mastery of technical terminology and explanatory precision. Yet, research on how STEM faculty perceive AI tools in their teaching remains almost scarce in Algeria and the MENA region. Thereto, this study reports descriptive findings from 30 faculty members at Ferhat Abbas University's Faculty of Technological Sciences, who are all teaching technical courses in English, using the TAAI-EMI questionnaire developed and validated for EMI contexts in non-Anglophone settings. The study examined five dimensions of attitudes toward AI in EMI: perceived usefulness, perceived ease of use, behavioral intention, self-efficacy, and anxiety. The questionnaire included 29 Likert-scale items rated from 1 to 5. Consequently, the results revealed the following: perceived ease of use (M = 4.04, SD = 0.86) > behavioral intention (M = 3.93, SD = 0.79) > perceived usefulness (M = 3.58, SD = 0.90) > self-efficacy (M = 3.38, SD = 0.93) > anxiety (M = 2.70, SD = 0.82). Moreover, item-level results suggest that respondents perceived AI as relatively easy to use and expressed relatively positive intentions to continue using it, while their judgments of usefulness and especially their confidence in using it effectively for EMI were more moderate. Anxiety was comparatively lower overall, but concerns pertaining to privacy, security and concerns AI translation accuracy for technical EMI terminology remained notable. Because the study used a small convenience sample (N = 30), the findings are descriptive and exploratory and should not be generalized to all Algerian university teachers or all STEM/EMI teachers. Nevertheless, the study suggests that teacher support should move beyond basic tool introduction and more toward AI literacy, EMI-specific pedagogical integration, confidence-building, accuracy checking, and responsible use.
Barriers, motivations and perspectives for AI integration in France and Tunisia
Amina Ben Mabrouk1; Hayet Saoudi2; Safia El-Bok3
Environmental and sustainability education (EESD) is a stated educational priority in both France and Tunisia. In the digital age, the rapid emergence of artificial intelligence (AI) opens up unprecedented possibilities for teaching EESD, but also raises uncertainties and ethical concerns. This contribution draws on an original quantitative survey conducted among secondary school teachers in France and Tunisia. The study examines teachers' actual EESD practices, their representations of digital tools and artificial intelligence, as well as the obstacles and levers that shape their relationship with the integration of these technologies. Obstacles include insufficient training, inadequate pedagogical resources, and overloaded curricula. The levers identified include personal commitment, awareness of climate issues, and interest in pedagogical innovation. The initial results reveal significant contrasts between the two countries. In France, teachers demonstrate a greater familiarity with digital tools, accompanied by a weariness with recurrent institutional pressures. In Tunisia, by contrast, a strong enthusiasm for EESD and AI is hindered by material constraints and limited access to available resources. Nevertheless, similarities emerge: teachers in both countries express a shared sense of a critical lack of targeted training on AI applied to EESD, as well as a fear that AI may exacerbate pre-existing inequalities between schools. Finally, the contribution offers concrete perspectives for a responsible, ethical and context-sensitive integration of AI into EESD, in line with the social responsibility of educational actors.
Implications for TeachersinCharge in early childhood education
Akiva Onika Sarita La Geer-Jeremiah1
Artificial intelligence (AI) is increasingly influencing leadership and pedagogical practices in early childhood education (ECE), creating both opportunities and challenges for Teachers in Charge (TiCs). This article examines the implications of AI integration for leadership, pedagogy, and ethical governance in preschool settings. Drawing on a systematic review of peer-reviewed literature published between 2020 and 2026, the study synthesizes evidence through the lenses of Sociocultural Learning Theory, Transformational Leadership Theory, and Ecological Systems Theory. The findings suggest that AI can enhance personalized learning, language development, adaptive instruction, and teacher professional learning when implemented within child-centred and relational pedagogical frameworks. However, concerns related to data privacy, algorithmic bias, equity, developmental appropriateness, and ethical governance remain significant barriers to responsible adoption. The review highlights the critical role of TiCs in fostering AI literacy, promoting pedagogical resilience, supporting ethical decision-making, and ensuring that technological innovation aligns with the developmental needs and rights of young children. The article argues that effective AI integration requires adaptive, collaborative, and ethically informed leadership approaches. Implications for policy, professional development, and future research are discussed, with particular attention to strengthening responsible AI use in ECE contexts.
Preliminary findings and challenges
Tatjana Jovcheska1
The rapid integration of digital technologies in education has redefined learning environments, requiring teachers to examine and adapt their teaching practices. This preliminary study, conducted as part of a doctoral research project, investigates ELT teachers' understanding of the digital pedagogy affordances, their current practices, and the challenges they encounter with their implementation in the language classrooms in North Macedonia. Current digital pedagogy frameworks emphasise the potential of digital technologies to support active learning, collaboration, differentiated learning, and multimodal meaning making. However, research indicates that many teachers have limited awareness of digital concepts and frameworks. Accordingly, this study looks into teachers' understanding of digital pedagogy, their intentional and/or unintentional application of its affordances, their perceived challenges, and their willingness to pursue further professional development. This small - scale study employed a quantitative research design to analyse ELT teachers' perspectives and experiences with digital pedagogy. The preliminary findings show that even though teachers demonstrate limited awareness of digital pedagogy affordances, many already integrate elements of these affordances into their teaching, often implicitly. Moreover, teachers express a strong willingness to extend their knowledge and develop their digital pedagogical competence. These findings underscore the need for professional development opportunities that help teachers move beyond the use of technology towards meaningful ELT practices.
The mediating role of organizational innovation
Sondeep Kaur1
Artificial intelligence (AI) is increasingly transforming higher education institutions by enhancing administrative efficiency, academic decision-making, and stakeholder engagement. Despite growing investment in AI technologies, limited research has examined how institutional AI readiness contributes to sustainability performance. This study investigates the relationship between AI readiness and sustainability performance in higher education institutions and examines the mediating role of organizational innovation. A quantitative research design was adopted using a cross-sectional survey of academic and administrative personnel employed in public and private higher education institutions (N = 500, synthetic demonstration dataset). Data were collected using a structured questionnaire that measured AI readiness, organizational innovation, and sustainability performance. AI readiness was assessed across technological infrastructure, human competencies, leadership support, and organizational preparedness, while sustainability performance was evaluated across economic, social, and environmental dimensions. The proposed research model was tested using Structural Equation Modeling (SEM) with maximum likelihood estimation and bootstrapping (5,000 resamples). The findings reveal that AI readiness has a significant positive direct effect on sustainability performance (β = 0.546, p < 0.001) and a significant positive effect on organizational innovation (β = 0.647, p < 0.001). Organizational innovation, in turn, significantly enhances sustainability performance (β = 0.314, p < 0.001) and partially mediates the relationship between AI readiness and sustainability performance (indirect effect = 0.202, 95% CI [0.141, 0.263]). These results suggest that innovative capability is a critical mechanism through which AI readiness translates into sustainable institutional outcomes. The study contributes to the emerging literature on AI-enabled transformation in higher education by providing empirical evidence of the strategic role of organizational innovation in achieving sustainability objectives. The findings offer practical implications for university leaders and policymakers seeking to leverage AI technologies to promote responsible innovation, institutional resilience, and long-term sustainable development.
Curriculum structures and trends of Singapore polytechnic diploma programmes
Liu Chaofan1
Artificial intelligence (AI) education is expanding rapidly across higher education, but existing research has focused predominantly on universities, AI literacy frameworks, and pedagogical innovation, with comparatively limited attention to curriculum design in practice-oriented higher education. Addressing this gap, this study examines Applied Artificial Intelligence diploma programmes across Singapore's five polytechnics through a qualitative, document-based comparative analysis of publicly available institutional materials, including programme descriptions, curriculum structures, module information, and learning outcomes. The analysis explores programme structures, emerging curriculum trends, and practice-oriented educational characteristics. The findings reveal substantial curricular convergence across institutions, with programmes consistently organized around programming, data analytics, machine learning, software development, and project-based learning, while also demonstrating meaningful institutional differentiation in technological emphasis, ethical orientation, industry integration, and interdisciplinary application. Some programmes foreground emerging technical ecosystems such as Generative AI, MLOps, intelligent automation, and deployment environments, whereas others place stronger emphasis on responsible AI, ethics, industry certifications, or interdisciplinary digital integration. The analysis further shows that experiential learning, workplace engagement, applied projects, internships, and industry responsiveness constitute core features of Applied AI education within Singapore polytechnics. The study argues that Applied AI curriculum development in practice-oriented higher education extends beyond technical training alone and increasingly reflects a hybrid curriculum model combining technical competence, applied learning, industry alignment, and responsible AI awareness. By focusing on Singapore's polytechnic sector, this study contributes to emerging scholarship on AI education by highlighting how curriculum architecture operationalizes AI education within diploma-level, non-university contexts.
Perspectives from Nigeria and Tanzania
Cynthia Adaeze Onuegwunwoke1; Joseph Paschal Mahona2
Artificial intelligence (AI) is rapidly reshaping teacher education, yet the perspectives of English language teacher educators in Sub-Saharan Africa had remained underexamined. This study investigated how teacher educators in Nigeria and Tanzania perceived, accepted, and integrated AI tools into their pedagogical practice. This was anchored on the Technology Acceptance Model (Davis, 1989) and the Technological Pedagogical Content Knowledge framework (Mishra & Koehler, 2006). A comparative descriptive survey was conducted with 120 educators (60 from Nigeria, 60 from Tanzania) using a six-subscale questionnaire (alpha = .76-.89) analyzed descriptively and inferentially. Teacher educators in both countries reported moderate to high perceived usefulness (Nigeria M = 3.73; Tanzania M = 3.60) and moderate perceived ease of use (Nigeria M = 3.05; Tanzania M = 3.22), with no significant cross-national difference. Both samples displayed a consistent TPACK gradient, with strong Content and Pedagogical Knowledge but weaker Technological Knowledge and integrated TPACK for AI. Tanzanian educators reported higher institutional support than their Nigerian counterparts (t(118) = -2.29, p = .024, d = -0.42), while ethical concerns around academic integrity and algorithmic bias against local languages were uniformly high in both countries. Lack of training was the most frequently cited barrier to adoption in Nigeria (73.3%) and Tanzania (68.3%). Regression analysis showed that perceived usefulness, perceived ease of use, TPACK, and institutional support each predicted intention to use AI (R2 = .48), and institutional support moderated the relationship between perceived usefulness and intention. These findings inform teacher education policy, curriculum reform, and professional development across Nigeria, Tanzania, and African contexts.
Nourchen Bouzidi1; Jamel-Eddine Gharbi2
This research attempts to identify the factors that explain the mismatch between university education and market needs in Tunisia. With reference to morphogenetic theory, we empirically verified the direct and interactive influence of the structure, culture and university agency on the adequacy between training and market needs. A survey was conducted with 240 respondents (120 students and 120 teachers). The results confirm the positive impact of structure and culture on the adequacy of university education and market needs. The agency's impact on students has been validated. On the other hand, agency among teachers does not have a significant effect. This research empirically verifies the morphogenetic theory. It shows that higher education in Tunisia is in morphostasis. It is in a structural and cultural determinism. Social interactions in academia are not yet capable of providing actors with the agency power and status to transform the dominant traditional structure and culture.
A TPACK-AI approach to transformative teacher professional development
Fouad Boulkroun1
The rapid integration of artificial intelligence (AI) into educational ecosystems has fundamentally disrupted the competency expectations placed upon classroom teachers, necessitating a critical re-examination of teacher professional development (TPD) frameworks. While the Technological Pedagogical Content Knowledge (TPACK) framework has long guided educators in navigating the intersection of technology, pedagogy, and content knowledge, its foundational architecture was not designed to accommodate the systemic, ethical, and epistemological dimensions that AI introduces into contemporary teaching and learning environments. This paper draws on the emerging TPACK-AI construct, which extends the foundational TPACK model by embedding AI-specific literacy competencies – including algorithmic awareness, data ethics, and human-AI collaboration – as distinct knowledge domains within a reconceptualised professional knowledge architecture. Employing a systematic conceptual review, this study examines how TPACK-AI reorients TPD from tool-centric technology integration toward deeper, sustained pedagogical transformation, addressing persistent gaps in existing literature including the absence of standardised AI competency benchmarks, inadequate formative feedback mechanisms, and inequitable program access. A central contribution is the development of a TPACK-AI TPD Checklist, operationalising the framework across six interrelated domains: foundational AI knowledge, algorithmic awareness and data ethics, pedagogical AI integration, human-AI collaboration, institutional alignment, and equity-responsive practice. The findings suggest that repositioning TPD within a TPACK-AI framework, structured through a domain-specific checklist, enables coherent, competency-driven professional learning pathways responsive to the ethical and contextual complexities of AI in education. This paper offers a theoretically grounded and practically actionable contribution for teacher educators, curriculum designers, and institutional leaders seeking transformative, equity-conscious outcomes in AI-integrated professional learning contexts.
Navigating AI transformation in Global higher education
Janvi Anand Rathi1; Shailendra Naresh Kulkarni1; Mensur Zeqiri1
Artificial intelligence (AI) is rapidly transforming higher education by reshaping teaching, assessment, research, governance and institutional decision-making. While generative AI offers opportunities for personalised learning, administrative efficiency and research support, its adoption also raises concerns regarding academic integrity, data governance, ethical oversight, equity, cultural representation and public trust. This conceptual and policy-oriented paper examines how higher education institutions can move beyond fragmented or reactive responses towards strategically governed, ethically responsible and socially inclusive AI implementation. Drawing on interdisciplinary literature from higher education policy, AI ethics, digital governance and critical knowledge-system perspectives, the paper introduces the Institutional Readiness and Ethical Integration (IREI) Model. The model assesses institutional preparedness across four dimensions: strategic readiness, operational readiness, ethical readiness and evaluative readiness. The paper identifies three institutional approaches to AI adoption: restrictive, experimental and strategically governed. It argues that sustainable AI integration requires transparent governance, human oversight, faculty and student capacity building, assessment integrity frameworks and culturally responsive policies. Particular attention is given to the risk that dominant AI systems may reproduce linguistic, cultural and epistemic inequalities, especially for institutions and learners in underrepresented regions of the Global South. The paper concludes that AI in higher education should be governed as a human-centred public good that advances academic quality, institutional trust, ethical accountability and inclusive global development.
A critical examination of contemporary template distortions
Jacob Filgona1; John Sakiyo2
Lesson planning is a central practice in teacher education and instructional design, yet contemporary lesson plan templates have undergone extensive modification without corresponding theoretical justification. Many templates now exhibit conceptual inconsistencies that distort the pedagogical purposes of their constituent components. This article introduces the concept of functional integrity—the principle that each lesson plan component should perform a distinct instructional function without duplication, substitution, or conceptual overlap with other components. It further proposes sequential coherence – the principle that lesson plan components should be ordered according to the temporal and logical dependencies of instruction. Drawing on instructional design traditions including behavioural objectives, instructional events, alignment theory, and teacher planning research, the article identifies three recurring threats to functional integrity: functional distortion (inappropriate information placed within a component), functional duplication (identical information repeated across components), and functional displacement (one component performing another's role). Through systematic analysis of common lesson plan components, the article develops a principle-based framework for evaluating template quality. The framework provides school administrators, teachers, teacher educators, and policymakers with diagnostic tools. In order to enable learner-centred pedagogy, formative assessment, and self-regulated learning, the study suggests that lesson planning quality should be evaluated based on the preservation of functional integrity and sequential coherence rather than template complexity or administrative completeness.
The role of organisational, social and policy innovations
Amira Fadhlaoui1; Hmaidi Khoubayeb2; Jamel-Eddine Gharbi1
The issue addressed in this study is the low level of collaboration between university research and teaching and the Tunisian business sector. The aim of the research is to test the influence of organisational, social and political innovation on university-industry collaboration. The data used to test our conceptual model are drawn from the Global Competitiveness Reports published by the World Economic Forum and the Global Innovation Reports published by WIPO. Regression analyses show that university-industry collaboration is significantly influenced by the three dimensions of innovation, namely organisational, social and political innovation.
Professional development challenges and education policy
Frida Alfred Mbwafu1
The integration of Artificial Intelligence (AI) is highly changing the way of teaching and learning everywhere around the world. In Tanzania, English teachers in lower public secondary schools are struggling to keep up with AI in their classrooms. However, many of them are not equipped with enough training to help them cope with the technological integrations. This study aimed to find out how ready the English teachers are for teaching with AI, and what challenges they face in terms of professional development and policy support. Using the TPACK framework, the main question was: Are Tanzanian public secondary school English teachers prepared for AI-powered classrooms, and what challenges stand in their way? Most research so far has focused on AI in universities and Western countries; there is little information about how prepared secondary school teachers in developing countries like Tanzania really are. To fill this gap, the study used interviews and group discussions with English teachers from three public schools. The results showed that teachers often do not know much about AI, have little access to technology, and rarely get in-service training. National policies on AI in schools are still vague. Many teachers are enthusiastic about AI's potential but feel unsure and unsupported as they try to make it work in their classrooms. The study suggests English teachers need ongoing training, better digital infrastructure, and inclusive AI policies. Ultimately, helping teachers feel confident and prepared is key to making AI-supported education fair and successful for every student in Tanzania.
New competencies for academic leaders
Sruthi Pillai1; Zunaith Ahmed2; R Ramakrishnan3
Artificial intelligence is transforming higher education with is growing use in institutional structures, decision-making strategies, academic work, and leadership roles. As institutions negotiate hitherto unheard-of technical change, Academic leaders are challenged to balance between creativity, moral responsibility, organizational resiliency, and stakeholder expectations Though there is growing interest in artificial intelligence in education, little study on the leadership skills required to guide institutions through AI-driven transformation has been done. This conceptual article develops an AI Leadership Competency Framework for institutions of higher learning. To enable academic leaders to gain the core competencies required to operate AI-enabled businesses, the study combines strategic leadership theory with digital transformation literature, change management, and responsible AI governance. The proposed framework consists of five connected skills: Digital literacy, Adaptive change leadership, Human-centered stakeholder engagement, Strategic AI vision, and Ethical governance. Long-term corporate results as well as institutional preparation for artificial intelligence implementation depend on these skills. Apart from technological knowledge, the study shows artificial intelligence leadership as a complex ability comprising moral judgment, organizational learning, trust-building, and strategic foresight. Good artificial intelligence leadership also helps a company be more flexible, creative, accepting of its staff, and able to provide better education. It also lowers hazards linked with administrative mistakes, transparency problems, and algorithmic prejudice. By offering a comprehensive leadership framework for AI-enabled companies, this study supports scholarship in management and higher education. It provides helpful guidance for college leaders, legislators, and educational leaders working to create leadership capacity in reaction to fresh technological disruptions. The framework also lays the groundwork for later empirical studies on the connection between AI leadership skills and institutional transformation results in higher education.
A management framework for governance, trust, and organizational transformation
Zunaith Ahmed1; Sruthi Pillai2; R Ramakrishnan3
Through uses in instruction, learning, assessment, administration, and institutional decision-making, artificial intelligence (AI) is revolutionizing higher education. AI presents great possibilities for personalization, efficiency, and innovation. But its use also sparks issues about openness, trust, governance, academic integrity, accountability, and organizational readiness. Most research concentrates on technical execution or pedagogical ramifications; little focus is paid to the organizational and management aspects of responsible artificial intelligence acceptance. This article presents a conceptual management framework for ethical artificial intelligence use in colleges and universities. Drawing from theories of organizational transformation, technology acceptance points of view, institutional governance data, and ethical innovation concepts, the framework pinpoints the key organizational elements influencing effective artificial intelligence integration. More precisely, the study contends that AI governance frameworks, moral standards, leadership dedication, stakeholder involvement, and institutional AI knowledge taken together determine how much a company trusts AI systems. Institutional trust mediates the relationship between these organizational conditions and responsible AI adoption, which in turn contributes to organizational transformation. The recommended model views are responsible for artificial intelligence use as a strategic organizational competence rather than mostly a technical endeavor. It stresses how universities could balance responsibility with invention while still ensuring compliance with society's expectations and educational standards. The study supports management scholarships by using responsible innovation theory in the context of higher education and offering leaders a working governance framework for institutions. The framework could enable colleges, legislators, and educational administrators to develop sustainable AI adoption strategies that enhance organizational performance while maintaining ethical standards, stakeholder trust, and educational quality in the age of artificial intelligence.
Opportunities and challenges
Nikos Tsipas1; Lamprini Seremeti1
Emerging technologies have radically transformed the modern educational ecosystem. Among these technological innovations, Business Intelligence (Business Intelligence, BI) is emerging as an increasingly important driver of educational transformation, supporting stakeholders in decision-making processes related to teaching, planning, quality assurance policies, and the administration of educational institutions. This study examines the role of BI in education through a synthesis of the existing literature. It focuses on the main arguments for and against the use of BI in educational settings, highlighting both its potential benefits and limitations. On the one hand, from a theoretical perspective, Business Intelligence is expected to support evidence-based decision-making, improve the monitoring of student performance, and enhance the effectiveness of educational institutions. On the other hand, in practice, its implementation in the educational routine raises concerns regarding data protection issues, the assessment of data quality, and the suitability of quantitative indicators for measuring complex educational effectiveness. By combining theoretical expectations with practical challenges, this study aims to provide a balanced understanding of how Business Intelligence can contribute to education.
Exploring generative AI for lesson planning and instructional innovation in EFL contexts
Djaber Oumaima1; Ghaouar Nesrine1
Generative artificial intelligence (GenAI) is increasingly being used as a support tool in educational practices, including lesson planning. This study aims to explore EFL teachers' perceptions of the use of generative artificial intelligence (GenAI) for lesson planning. To achieve this aim, a questionnaire was distributed to 25 EFL teachers at Badji Mokhtar Annaba University, Algeria, to examine their use of GenAI, perceived benefits, challenges, and recommendations for its effective and responsible integration into lesson planning. The findings show that GenAI was widely used by the participants, particularly for designing classroom activities, preparing teaching materials, and developing quizzes and assessments. Teachers generally reported positive perceptions, especially regarding time saving, faster idea generation, and the development of engaging activities. However, they also identified concerns related to accuracy and reliability, mismatch with students' proficiency levels, and generic or repetitive outputs. Teachers therefore recommended verifying AI-generated content, maintaining professional judgement, using AI as a supplementary tool, and developing training and prompting skills. These findings suggest that GenAI can support lesson planning effectively when combined with teachers' pedagogical knowledge, critical evaluation, and professional judgement.
Mandu S. Umoren1; Taiwo E. Adeleke1
This study examined the influence of artificial intelligence (AI) readiness on non-academic staff's skill rebalancing at the University of Uyo, Nigeria. Specifically, the study investigated the levels of skill rebalancing and AI readiness as well as the relationship between AI readiness (in terms of data, technological infrastructure, and AI policy) and non-academic staff's skill rebalancing at the University of Uyo. Two research questions and two hypotheses were developed to guide the study. A descriptive survey research design was adopted. The population comprised all senior non-academic staff of the University of Uyo. A sample of 731 respondents was purposively selected for the study. Data were collected using a researcher-developed instrument entitled Artificial Intelligence Readiness and Non-Academic Staff's Skill Re-Balancing Questionnaire (AIRNASSRQ). The instrument was validated by experts in Educational Management and Measurement and Evaluation, while its reliability was established using Cronbach's Alpha, yielding a coefficient of 0.71. The results revealed a moderate level of skill rebalancing among the non-academic staff at the University of Uyo (mean = 2.67, SD = 0.840), while the level of artificial intelligence readiness was low (mean = 2.38, SD = 0.65). Also, a significant positive relationship between artificial intelligence readiness and non-academic staff's skill rebalancing was found in the understudied university. The multiple regression analysis revealed that the dimensions of artificial intelligence readiness jointly predicted the non-academic staff's skill rebalancing significantly. The study concluded that artificial intelligence readiness is a significant determinant of non-academic staff's skill rebalancing in universities. It was therefore recommended, among others, that the university management should strengthen AI-related capacity-building programmes, improve digital infrastructure and internet connectivity, and enable non-academic staff to adapt effectively to the changing technological demands of university administration.
A study of Tunisian teachers' perceptions through a storytelling approach
Lilia Smida1; Ibticem Elhaj Fraj Ben Zammel2; Jamel-Eddine Gharbi3
The transition from traditional education to sustainability-oriented education promotes creativity, critical thinking, and human development. Integrating critical thinking across teachers, students, and content is essential. Hybrid models combining artificial intelligence tools with human teaching, as complementary pedagogical approaches, are recommended to enhance learning outcomes and critical thinking (Fakour & Eimani, 2025) while preserving human and emotional support (Smida, 2026). Strengthening digital literacy and critical thinking is therefore crucial for educators to adapt to AI-driven transformations in education. In examining the role of teachers in the era of AI integration, AI is perceived by most participants as a facilitator that enhances teachers' efficiency by providing educational resources and automating routine tasks, thereby reducing time-consuming activities and enabling educators to focus on higher-value pedagogical functions. However, unequal access to AI between teachers and students represents a significant risk that may reinforce existing educational inequalities. Consequently, universities have a responsibility to address these disparities and ensure that AI integration promotes equity rather than exclusion (Hmissi, 2026). Teachers' perceptions of AI in education vary according to their level of familiarity with the technology. Those with greater knowledge of AI tend to view it as a transformative educational tool, whereas less-informed teachers are more likely to express concerns and reservations (Laouel, 2025). Similarly, the use of AI among teachers at the Higher School of Design Sciences and Technologies in Tunisia is unequal, with only some integrating it effectively into their pedagogical practices. This disparity is mainly explained by differences in technological familiarity, limited training, and the absence of clear institutional frameworks. As a result, AI adoption ranges from basic supportive use to more advanced pedagogical integration (Fatnassi, 2026). These disparities exist not only within the same institution but also across different universities. Exploring differences between private and public higher education institutions may provide novel insights into AI adoption in higher education, particularly through a storytelling approach (Leah et al., 2010), revealing similarities and differences in teachers' familiarity with AI use and AI integration.
Constrained, collective, and improvised forms from the Global South
Jacob Filgona1; John Sakiyo2
Dominant theories of teacher agency have been developed primarily within Global North contexts characterized by relative resource adequacy, policy stability, and individual professional autonomy. Yet, the majority of the world's teachers, coming from the global south, work under conditions of chronic resource scarcity, policy incoherence, and systemic constraint—conditions these theories were never designed to explain. This paper addresses a central question: what forms does teacher agency take when these conditions do not hold? Drawing on literature from Nigeria, Ghana, South Africa, Zambia, and Kenya, this conceptual study develops a tripartite framework of agency in conditions of constraint. It conceptualizes improvised agency as real-time creative response when formal systems are inadequate, collective agency as forms of action distributed across professional networks rather than located in individuals alone, and constrained agency as professional action exercised within severe structural limitations rather than the absence of agency. The paper further proposes a dynamic relational model in which structural constraints generate conditions for improvisation, improvisational practices are sustained and refined through collective professional networks, and collective forms of action may, over time, contribute to the partial renegotiation of structural constraints. This framework builds on ecological accounts of teacher agency by shifting attention to how agency is negotiated in contexts marked by persistent scarcity and unstable institutional conditions. In doing so, it offers a lens for interpreting curriculum reform beyond stable policy environments. The paper concludes by considering the implications of this perspective for curriculum policy design, teacher professional development, and comparative studies of educational change.
A study of Jharkhand universities
Nilesh Kumar1
The use of Artificial Intelligence in education is changing the way we teach learn and run university system. Artificial Intelligence is making a difference in education all over the world. In India the government has introduced the National Education Policy 2020 which is pushing universities to use technologies and Artificial Intelligence in education. This study looks at how ready faculty members are to use AI tools in colleges and universities in Jharkhand. The research is trying to figure out how much faculty members know about AI tools, what skills they have and how they feel about using Artificial Intelligence in their teaching. It also tries to find out what problems faculty members are facing, like not getting training not having good enough computers and equipment not having clear instructions from universities. The research employs a quantitative methodology utilizing structured surveys through questionnaires distributed to faculty members from chosen universities and colleges in Jharkhand. Statistical methods will be employed to examine the relationship between faculty preparedness and variables like age, teaching experience, digital competency, institutional backing, and previous encounters with AI technologies. The study's findings aim to offer important insights for policymakers, university leaders, and educational planners in creating effective faculty development initiatives. The research will additionally add to the expanding academic literature on AI implementation in Indian higher education, especially concerning developing states such as Jharkhand.
Basil Osayin Daudu1; Salaudin Usman2; Michael Olanrewaju Oluyori2; Peter Anakobe Adinoyi3
In 2022, the National Universities Commission (NUC) introduced the Core Curriculum and Minimum Academic Standards (CCMAS), and its implementation in Nigerian universities started in 2023, replacing the outdated and rigid content-based curriculum, Benchmark Minimum Academic Standards (BMAS). However, educationists and non-educationists alike have concerns about the feasibility of CCMAS. This paper investigates whether CCMAS is outcome-based (problem-solving), as claimed by NUC, or content-based, like the old one. Therefore, using mixed methods, the study sampled university lecturers, students, and education analysts for this investigation because of their direct involvement and expertise. The responses generated from these participants were analyzed and interpreted using analytical and statistical techniques. Therefore, among others, the paper recommends consistent review and monitoring of CCMAS compliance to ensure quality and practical knowledge in the best interest of higher education in Nigeria.
A narrative review of teacher readiness
Dada Tolulope Racheal1
The rapid global adoption of artificial intelligence (AI) in education offers transformative opportunities to enhance pedagogical practices and optimise student learning outcomes. However, successful AI integration in secondary school classrooms fundamentally depends on teachers' readiness to adopt and apply these technologies effectively. This narrative review systematically synthesises the existing scholarly literature on teacher preparedness for AI integration in Nigerian secondary schools. Through a comprehensive analysis of 28 peer-reviewed empirical studies published between 2015 and 2025, the review establishes that Nigerian secondary school teachers demonstrate foundational awareness of AI concepts and tools, yet exhibit critical deficiencies in practical implementation skills. Key barriers include chronic shortages of reliable technological infrastructure, restricted access to structured professional development programmes, and the complete absence of national or institutional policy frameworks to guide AI adoption (Sambo-Magaji et al., 2025; Eke, 2024). The review further identifies urgent intervention priorities: mandatory AI-literacy training modules tailored to subject-specific pedagogy and the urgent formulation of clear, enforceable policy guidelines at both institutional and national levels. By clarifying the multifaceted dimensions of teacher readiness, this paper delivers evidence-based recommendations for policymakers, school administrators, and teacher educators to achieve equitable and sustainable AI integration in Nigerian secondary education.
An activity theory perspective
Beibei Ren1
Writing strategy research has undergone an epistemological turn from cognitive to sociocultural in recent years. Activity Theory (AT) represents a powerful framework to examine writing strategies from a sociocultural perspective. Though AT has been applied to investigate strategies writers employ in various written genres, how pre-service teachers employ strategies when writing Teaching Philosophy Statements (TPSs) for the first time has yet received scholars' attention. To address the research gap, this study conducts a qualitative study probing the technology-related mediating strategies in EFL pre-service teachers' TPS writing. The study collects multiple sources of data, including nine TPSs written by nine EFL pre-service teachers (one from each) and one-on-one interviews with the participants. As one variety of artefact-mediated strategies, a range of technology-related strategies are employed by the pre-service teachers. First, findings show that the pre-service teachers resort to the Internet, including such platforms as Google and YouTube, to gather information related to the TPS genre, which mediate their conceptualization of the TPS. Findings also demonstrate that the participants employ different kinds of technology-assisted writing tools when writing the TPSs, including using Youdao for vocabulary issues, Grammarly and Quillbot for grammar-related issues, and DeepL to help generate natural English expressions. This study yields important insights regarding EFL students' orchestration of technology-related writing strategies when they are novice writers of the TPS genre.
Opportunities, challenges, and institutional implications of AI tools in classroom teaching
Houda Fadel1
The rapid proliferation of Artificial Intelligence tools in educational settings is fundamentally reshaping the landscape of classroom teaching. From intelligent tutoring systems and automated feedback platforms to generative AI assistants, these technologies are no longer peripheral add-ons but increasingly central to how instruction is designed, delivered, and experienced. This paper offers a theoretical and conceptual examination of the integration of AI tools in classroom teaching, with particular attention to its pedagogical, ethical, and institutional dimensions. Drawing on contemporary educational theory — including constructivist and socio-cultural frameworks — this paper interrogates how AI tools alter the traditional roles of teacher and learner. It argues that while AI can personalize learning experiences, scaffold complex tasks, and reduce administrative burdens on educators, its uncritical adoption risks undermining core pedagogical values such as critical thinking, learner autonomy, and meaningful human interaction. The paper further explores the institutional challenges that arise as schools and universities attempt to incorporate AI into their curricula and governance structures. It examines tensions between innovation and regulation, equity of access, and the need for coherent institutional policies that balance technological affordances with educational integrity. Finally, the paper proposes a conceptual framework for what may be termed "AI-conscious pedagogy" — an approach that positions the educator not as a passive user of AI tools, but as a critical mediator who thoughtfully integrates technology in alignment with clear learning objectives and ethical responsibilities. This contribution aims to advance the theoretical discourse on AI in education and to inform policymakers, curriculum designers, and practitioners navigating this transformative moment in the history of teaching.
Mahona, Paschal Joseph1; Cynthia Adaeze Onuegwunwoke2
This study examined the transformation of teacher education in Africa within the context of the growing integration of artificial intelligence (AI) technologies into teaching and learning processes. Focusing on Nigeria and Tanzania as comparative case studies, the paper explored how AI-driven tools reshaped pedagogical practices, teacher preparation, professional identity, and ethical responsibility in higher education. The study was anchored on the Technological Pedagogical Content Knowledge and Sociocultural Theory, which provided theoretical explanations for technology integration, collaborative learning, and culturally responsive pedagogy in contemporary educational environments. The population of the study comprised student teachers and teacher educators from selected teacher education institutions in Nigeria and Tanzania, including Alvan Ikoku Federal University of Education, Owerri, Imo State, Nigeria. The paper adopted a qualitative-comparative approach to investigate the opportunities and challenges associated with AI integration in teacher education. The findings highlighted the pedagogical benefits of AI like personalized learning, instructional support, digital literacy development, and enhanced access to educational resources. Simultaneously, the study interrogated critical concerns relating to ethical literacy, technological inequality, overdependence on automated systems, and the preservation of human-centered teaching values within African educational contexts. The paper argued that effective teacher education in the AI era required a balanced framework that combined technological competence with ethical consciousness, intercultural awareness, and contextual responsiveness. The study concluded that African universities needed to strategically redesign teacher education curricula to prepare educators who could responsibly and critically engage with AI-driven educational transformation.
Challenges and issues for North African universities
Aziz Hantem1
Artificial intelligence (AI) has experienced remarkable growth driven by three converging factors: increasing processor power, the expansion of big data, and continuous algorithmic improvement (Portnoff & Soupizet, 2018; Ndione, 2022). Technologies such as machine learning, computer vision, and natural language processing (NLP) have progressively transformed many sectors. However, while the private sector has capitalized on these advances, public institutions particularly higher education establishments in developing countries, still lag significantly behind in AI adoption (Fokou, 2021). This paper examines the integration of AI in public management, with a focus on North African universities. It pursues two objectives: first, to identify the key challenges and barriers to AI adoption within these institutions; second, to analyze the causal relationships between AI adoption and improvements in university performance, both in terms of managerial governance and pedagogical innovation. The paper also proposes a conceptual model linking the determinants of AI adoption to organizational performance outcomes. The central research question is: to what extent does AI adoption contribute to improving the performance of North African universities, and what are the main facilitating and inhibiting factors? Theoretically, this research draws on the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT). Methodologically, a qualitative exploratory approach is adopted, combining a systematic literature review with participatory observation.
Transforming pedagogical beliefs and enhancing learner performance in the Algerian higher education context
Houyem Gasmia1; Farida Sadoune2
The rapid proliferation of artificial intelligence and digital technologies has fundamentally redefined expectations for language teachers worldwide. The capacity of English as a Foreign Language (EFL) teachers to integrate information and communication technologies (ICT) into their practice has emerged not merely as a professional enhancement, but as a prerequisite for pedagogical relevance in AI-mediated environments. This imperative is particularly pressing in Algeria, where systemic constraints have historically limited teachers' exposure to technology-enhanced professional development. This paper examines, from a theoretical standpoint, the conditions under which ICT-based in-service EFL teacher training may effectively transform pedagogical beliefs and promote learners' academic performance within Algerian higher education. Drawing on an integrated multi-theoretical framework — encompassing the Technological Pedagogical Content Knowledge model (TPACK; Mishra & Koehler, 2006), the Technology Acceptance Model (TAM; Davis, 1989), Vygotsky's sociocultural theory (1978), and Ertmer's first- and second-order barrier framework (1999, 2010) — the paper explores how these perspectives illuminate the mechanisms through which teachers construct and revise their beliefs about technology integration. It further argues that ICT-based training — when designed to address both infrastructural and belief-based barriers — holds significant potential for repositioning EFL teachers as agentic adopters of digital and AI-enhanced pedagogies. This argument is situated within broader discourses on digital equity, institutional change, and the ethical governance of AI in education, with particular attention to MENA and Global South contexts where such transformations remain undertheorized. Implications for EFL curriculum reform, teacher education policy, and the responsible integration of AI in language education are critically examined.
Constantine Passaris1
Artificial Intelligence (AI) represents a transformational innovation with profound implications for higher education. Since time immemorial higher education has played a foundational role in disseminating knowledge and empowering scientific advancement. AI has emerged as a transformative force in all fields of human endeavour and especially in higher education. Academic institutions worldwide must create new pathways and novel pedagogical paradigms by integrating AI into teaching, learning, scientific research, and academic governance models. In consequence, redefining the academic ecosystem for a new era with innovative technological enhancement. AI is the contemporary phase of internetization. Internetization is a new word and concept that I have coined to describe the electronic empowerment and global outreach of the 21st century. This paper will examine AI's impact on pedagogy, research, and academic governance. It will propose a framework for AI's responsible integration into academic institutions. In effect, mapping a road map that creates a blueprint for how AI can enhance teaching and learning, accelerate scholarly inquiry, and invigorate institutional governance. The paper's overarching conclusion is that higher education must embrace AI as a catalyst for innovation while proactively addressing risks related to academic integrity, algorithmic bias, and the need for ethical guardrails that safeguard human centered learning.
Roland Berberich
UK Higher Education Institutions (HEIs) find themselves in a seemingly existential crisis. Facing a spiralling decline in international recruitment already, the advance of generative AI (GenAI) disrupts and threatens traditional HEIs. Institutions and leaders struggle to a consistent and coherent response. Reactive "more of the same" or aggressive cost-cutting and staff reductions, are simply admitting defeat. Simultaneously deflecting accountability onto allegedly unforeseeable global instability and obstructive policymaking does not address the problem of a value proposition undermined by reality. This conceptual paper examines the underlying structural nature of this crisis and its impact on UK HEIs. Moving beyond superficial defence, this paper argues that while macroeconomics and geopolitics have undeniably disrupted domestic campuses, the true vulnerability lies with denial and resistance to change. Absence of strategic thinking on matters of internationalisation, institutions devoid of bold leadership became exposed to a perfect storm. Failing to proactively engage with GenAI and human intelligence, holding on to outdated concepts combined with a rapidly unravelling prerequisite for current internationalisation patterns HEIs find themselves in a desperate struggle for survival. The paper concludes offering a series of interventions for UK HEIs. Institutions must completely rethink their core value proposition. The future of higher education is argued to be surprisingly (non-)artificial. Survival hinges on a deliberate return to deep, human-centric, and relational education combined with sound strategy and decision making. Cultivating uniquely human capacities such as ethical judgment, criticality, reflexivity, and collaborative development universities can reclaim their distinct value in a world increasingly shaped by AI.
How AI personalization shapes university image and student choice
Shiva Rani1
For the past decades, universities have been increasingly inviting public figures, industry leaders, influencers, motivational speakers, celebrities, and entrepreneurs. The aim behind that university is to develop the brand, increase visibility, global ranking and student engagement. Specifically, before COVID, the university focused on academic quality, branding, internationalization and student experience. However, after the pandemic Events, institutional digital visibility, experiential marketing and reputation are increasing. The current study is based on the S-O-R model, which represent there is a stimulus that leads to the organism and response. In the current study, the AI personalize mega event act as a stimulus, student perception as organism response lead to university choices. The objective of this paper is to investigate the impact of AI personalization event and mega events on perception. Another objective is to investigate the impact of perception on university choices. The study was conducted in the Indian Context, specifically in the Uttarakhand context. The study used a quantitative research design and stratified purposive sampling. The study used 12th, graduate and post-graduation students of the last year. The study used a priori G Power software for sample size measurement. The study used partial least squares-structured equation modelling. The study found that there is a positive relationship between the variables. The study found that the perception has a partial mediation relationship between mega event and university choices. This study is play significant role in higher education marketing.
A performance analysis of top 25 NIRF 2025 universities
Debdas Mondal1
This study analyses the research performance, collaboration patterns, and academic impact of the Top 25 universities ranked in NIRF 2025 using multiple indicators derived from Scopus and Web of Science databases for the period 2020–2024. The analysis examines research output, citation impact, h-index values, international collaboration, funding patterns, subject distribution, and highly cited papers to understand the research landscape of leading Indian universities. The findings show that institutions such as the Indian Institute of Science, University of Delhi, Banaras Hindu University, and Jadavpur University dominate in terms of publication volume, citations, and overall research influence. Public universities constitute the majority of the top-ranked institutions, although private universities such as Manipal Academy of Higher Education, Birla Institute of Technology and Science Pilani, and Vellore Institute of Technology demonstrate increasing research productivity and international collaboration. The results further reveal that research output is largely concentrated in engineering, natural sciences, and medical sciences, while social sciences and humanities account for a smaller share of indexed publications. International collaboration, particularly with institutions in North America and Europe, contributes significantly to higher citation impact and the production of highly cited papers. Overall, the study highlights the importance of research productivity, collaborative networks, and global engagement in strengthening the academic performance and international visibility of India's leading universities.
A comparative study of institutional EMI policy and teacher agency in the MENA region and Global South
Soundous Benkorichi1
The adoption of large-scale educational policies often prioritizes measurable performance indicators over the complex, localized realities of classroom pedagogy. This study investigates the institutional implementation of English-medium instruction (EMI) in Algerian higher education, providing a critical MENA-region perspective on the global 'Internationalization of Higher Education' (IoHE) movement. Drawing on in-depth qualitative interviews with ten university instructors, this research identifies a persistent 'administrative-pedagogical disconnect.' Findings reveal that when institutions focus primarily on 'rushed' implementation and bureaucratic reporting, they trigger a 'tension gap' that neglects both student engagement and the professional expertise of faculty. This study explores the cognitive and professional negotiation processes instructors undergo when caught between globalized policy demands and the immediate, linguistic-pedagogical requirements of the classroom. Ultimately, this research argues that current institutional adoption models—often mirrored in the Global South—frequently overlook the essential 'human layer' of education. By highlighting the friction between top-down governance and bottom-up instructional enactment, the paper offers a critical critique of metric-driven reform. It concludes that sustainable educational policy must prioritize pedagogical sensitivity and teacher agency to ensure that institutional change translates into meaningful classroom success rather than mere administrative compliance.
Evaluating teaching quality and experience recognition in India's coaching-to-academia transition
Rohit Kumar Pal1
India's coaching industry has emerged as a significant parallel educational ecosystem, producing successful candidates in competitive examinations such as NEET, JEE, UPSC, and other national-level entrance tests. Many teaching professionals in this sector possess highly prestigious qualifications from institutions such as Indian Institutes of Technology (IITs) and Indian Institutes of Management (IIMs), alongside formal credentials including B.Ed and UGC NET/SET. Despite their proven teaching effectiveness and domain expertise, many are rejected from school, college, and university teaching positions due to limited recognition of coaching-sector experience. This study examines whether teaching quality can be measured solely through formal credentials such as B.Ed and NET/SET or whether practical teaching outcomes and experience should receive greater institutional recognition. The study analyzes data collected from 552 teachers and 337 educational administrators across India. Findings reveal that 45.1% of applicants were rejected despite substantial teaching experience, while administrative hiring policies remained heavily credential-oriented. Statistical analysis demonstrates a significant disconnect between practical teaching effectiveness and formal recruitment standards. Administrators acknowledged regulatory constraints linked to affiliation norms, API scores, and statutory compliance requirements. The research argues for a balanced evaluation framework integrating teaching outcomes, domain expertise, and professional experience alongside formal qualifications. The study contributes to policy discussions on teacher recruitment, educational quality assessment, and experience-equivalency frameworks within India's evolving education system.
Exploring techno-pedagogical competence, ethical awareness, and social responsibility in teacher education
Nazish Khan1
Artificial Intelligence (AI) is bringing major changes in the field of education. Teaching methods, assessment processes, classroom interaction, and digital learning practices are increasingly influenced by AI-based tools and technologies. In teacher education, these changes have created both opportunities and challenges for prospective teachers. The present study examines the role of professional development in improving techno-pedagogical competence and ethical responsibility among prospective teachers in the context of AI-supported education. The paper focuses on the need to prepare future teachers who can use technology effectively with proper pedagogical understanding and social responsibility. It explains that prospective teachers need continuous professional development to adjust to AI-based teaching tools, online platforms, and changing classroom practices. Along with technological skills, teachers also require ethical awareness, reflective thinking, decision-making ability, and responsible use of AI in educational settings. The study is conceptual and analytical in nature and is based on current discussions related to AI in teacher education and professional competency development. The paper states that teacher education programmes should give equal importance to technology, pedagogy, ethics, and human values. AI can support teaching and learning processes, but it cannot replace human interaction, emotional understanding, and professional judgement in education. The study adds to present academic discussions on AI, teacher education, and responsible educational practices, especially in the context of developing countries.
Boulmerka Soulef1
The current study investigates EFL teachers' attitudes toward Artificial Intelligence (AI) implementation at the Ecole Normale Supérieure (ENS) of Constantine, Algeria. The study highlights a tension between enthusiasm for perceived pedagogical benefits and apprehensions concerning ethical, technical, and institutional hurdles. This qualitative enquiry involved ten instructors chosen via purposive sampling, ensuring various expertise. Semi-structured interviews highlighted three principal themes: advantages (lesson design, engagement), ethical issues (privacy, over-reliance), and institutional limitations (lack of training/support). The findings highlight that for instructors to responsibly implement artificial intelligence (AI) tools in language classes, they require clear institutional regulations, ongoing professional training, and well-established ethical frameworks. While AI offers opportunities for personalized feedback and automated instruction, its integration introduces significant risks regarding data privacy, and the potential loss of human interaction in teaching.
Higher education students' views on AI, learning and the future of work
Pedro Pinheiro1
Artificial intelligence is transforming higher education and work, but students' perceptions of benefits, risks and responsible use remain uneven. This study examines how higher education students understand, use and evaluate AI tools in academic and professional contexts. Drawing on survey data from 104 respondents, mostly undergraduate students in management, business, economics and finance, the research explores AI experience, perceived impacts, academic uses, ethical concerns and readiness for AI-supported decision-making. The findings indicate a pragmatic but cautious attitude. Most respondents see AI as useful yet risky, and identify technology, marketing, education and finance as areas of major impact. While self-assessed knowledge of AI is moderate, students associate AI with workplace efficiency and support for learning, especially through research assistance, summarisation, idea generation, personalised learning and clarification of complex topics. At the same time, trust in AI for business decision-making and confidence in distinguishing generative AI from other AI systems are weaker. Respondents express concern about over-reliance, job loss, privacy, transparency and algorithmic bias. In educational terms, students support the integration of AI into university courses and strongly favour institutional guidelines for ethical academic use. The study contributes to interdisciplinary debates on AI in education, culture and society by showing that students are not simply enthusiastic adopters or resistant users. Rather, they combine frequent use with a demand for responsible governance, pedagogical guidance and critical AI literacy. These results suggest that higher education institutions should move beyond access to tools and develop curricula integrating technical, ethical and professional dimensions of AI competence.
Roland Berberich1
After panic-induced return to in person exams and increased AI policing, it appears Higher Education Institutions (HEIs)are ready to raise the white flag of surrender. No credible attempt appears to be made to genuinely question the value proposition of a university education in the 21st Century. While the silver lining of a Covid vaccine spurred vocal "back to the classroom" movements, the rise of Generative AI (GenAI) again sees HEIs ignoring their own teachings and increasingly desperate efforts to maintain the classroom and essay comfort zone for academics that have seemingly forgotten the crucial nature of their role. A global pandemic or technological advancements are not just disruptors or an inconvenience, they have become catalysts for cataclysmic change that forces HEIs at all levels to reframe their value to an increasingly critical audience. By illustrating the many failures of the higher education business this paper demonstrates that institutional disengagement, credentialism, and the commodification of degrees alienated the audience long before GenAI. The paper proposes to abandon assembly line transactionality where students rightfully utilise GenAI to fulfil bureaucratic mandates. Instead, a return to transformative experiences that embed GenAI as a component the learner can critically engage with. Continued deflection away from failed strategic leadership only delays rebuilding a transformative, collaborative scholarship experience which GenAI cannot simulate.
Samson Mutsagondo1
Zimbabwe unveiled its National Artificial Intelligence Strategy on 13 March 2026. The development was hailed as a significant step towards artificial intelligence (AI) adoption in the country. This study interrogates the impact of the national strategy on AI adoption in records management in Zimbabwe. The study examines the contribution of each of the six core pillars of the strategy in relation to AI adoption in records management. The pillars are talent development, infrastructure, service transformation, governance, research, and strategic collaboration. The strategy lays a solid foundation for AI adoption and implementation. In addition, it enhances technological sovereignty and the protection of domestic data assets, and seeks to develop national infrastructure, skills and collaborations, from which records and archival institutions may greatly benefit. These factors are set to rescue Zimbabwe from the low AI adoption and implementation tier on the world AI readiness and adoption rankings. Nonetheless, the study reveals many implementation gaps owing to the discrepancy between theory and practice. Among the national strategy's shortfalls in records management are skills deficiencies, regulatory lapses, low capital investment, chronic power shortages, and tight fiscal constraints, thus making a mismatch between strategic ambition and execution capacity. The study serves as a critical reminder of the importance of political will and commitment, and resource mobilisation and alignment for the country to make meaningful strides in AI adoption and implementation. Data in this study were obtained from the review of literature and documents on AI, national AI strategy and policy and AI adoption and implementation.
Incorporating AI into journalism school curricula
Shawn McIntosh1
Digital media has brought disruptions for decades now to the journalism profession and the news industry, and AI promises—or threatens—even more disruptions. Nevertheless, AI tools are being increasingly incorporated into newsroom workflows even as the ethics of AI and best practices using AI are very much still evolving. The challenges of AI are compounded in an educational setting, where students may frequently use AI in courses, and may not have the same negative perceptions around its use that faculty (and some professionals) may see. This paper addresses the difficult and delicate balancing act that journalism schools face in teaching and preparing journalism students to utilize AI to do better journalism while not undermining the fundamental critical writing, thinking, and analytical skills journalists increasingly need to do their jobs well. The paper argues that it is an educational disservice for journalism schools to ignore or downplay the role of AI in the profession, as students must be prepared for a world in which AI tools are widely used. Furthermore, without giving students the faculties by which to critically examine how AI is used, the effects of AI on their work, how AI affects public trust in journalism, and how AI will further change the professional roles of journalists themselves, it is likewise leaving them unprepared when entering the profession. The paper explores ways this educational balance may be achieved by examining some successful cases of journalism schools incorporating AI into their curricula.
Artificial intelligence as a catalyst for CLIL transformation in Algerian higher education
Benabdelouahab Amina1; Amrate Mustafa2
Content and Language Integrated Learning (CLIL) has gained attention in higher education due to its capacity to develop disciplinary knowledge and English language competence within an integrated pedagogical framework. In the Algerian context, implementation faces structural and pedagogical constraints that shape classroom realities. These constraints include uneven linguistic proficiency among learners and limited instructional resources, alongside pressures linked to academic internationalization. Recent advances in Artificial Intelligence (AI) introduce for pedagogical innovation and curriculum enhancement. Moreover this study examines potential of AI as a catalyst for transforming CLIL practices in Algerian higher education. A qualitative exploratory design guides process to capture academic experiences. Data were collected through semi-structured interviews with fifteen university teachers engaged in English-medium instruction and CLIL-related practices at an Algerian university. Thematic analysis structured data and highlighted perspectives, opportunities, and challenges related to AI integration in CLIL contexts. Furthermore findings indicate that AI-based tools extend pedagogical scope of CLIL in ways. They support adaptive learning pathways and provide immediate feedback systems. They also offer instructional support and refined assessment practices. Participants describe how these tools strengthen learner autonomy, enrich classroom interaction, and facilitate engagement with disciplinary content. Additionally the analysis further shows that AI contributes to reshaping institutional practices and strengthening academic quality while supporting educational reform agendas. Ethical concerns, digital infrastructure limitations, and teacher preparedness emerge as conditions shaping implementation. The study positions Artificial Intelligence as a catalyst for the evolution of CLIL ecosystems in Algerian higher education and highlights implications for future pedagogical development.
A workshop model for responsible self-presentation
Kai Löser1
Application processes are increasingly shaped by AI on both sides: organizations use automated tools to screen, rank, or evaluate applicants, while applicants themselves use chatbots to draft their materials. This dual AI mediation creates a pedagogical problem for career guidance: students need to understand how they may be assessed while learning to use AI without surrendering privacy, integrity, or their own voice. This contribution examines "Bewerben mit KI" (Applying with AI), a three-hour workshop developed at the University of Rostock and proposed for adaptation to study and career guidance in schools. The workshop offers a practice-based design for AI literacy in application coaching. It introduces generative AI as probabilistic text production and treats applications as situated self-presentation rather than template completion. Participants analyze job advertisements, translate competencies into biographical evidence, compare generic and personalized cover-letter drafts, and simulate interview situations. Each step includes critical checks: What became clearer? What became exaggerated or generic? Which data should not be shared? When does assistance become misrepresentation? In a final reflection, participants formulate a personal rule for AI use: what they delegate, what remains their own, and how privacy and integrity are protected. Transferring the format to secondary schools would require age-appropriate moderation, data minimization, and institutional safeguards. The contribution argues that responsible AI use in career guidance is not a matter of better prompting alone, but of structured comparison, deliberate limits, and reflection.
Empirical evidence, competencies, training needs, and pedagogical challenges
Sophia Crescentini1; Marta Salvucci2
The rapid and massive integration of Generative Artificial Intelligence (AIG), particularly via user-friendly chatbot applications, are shifting human experiences into an "onlife" hybrid environment. In higher education, this widespread yet spontaneous adoption introduces important pedagogical challenges, including the risk of cognitive outsourcing, epistemic deprivation, and an inadequate conception of knowledge and learning. To explore the current state of AI literacy, the contribution presents an empirical study conducted at the University of Urbino Carlo Bo. The study utilized an adapted Italian version of the standard SDSU (San Diego State University) AI Survey, specifically analyzing responses from 38 professors and 74 students regarding competencies and institutional training. The findings reveal a significant cultural and institutional gap. While 65% of students feel confident using AI, their security is tied to practical usage rather than algorithmic understanding. Conversely, only 30% of professors feel capable of explaining basic AI mechanics, and 72% report a profound lack of adequate institutional training. Furthermore, 82% of students declare to operate in a normative "grey area" without clear guidelines. Despite these shortcomings, strong interest exists for future integration: 88% of students demand practical AI workshops for their professional curricula, and 60% of faculty support mandatory training modules, while expressing concerns over increased workloads. Bridging this gap requires moving beyond mere instrumental mastery toward a comprehensive "AI literacy" framework. In particular, the paper advocates for robust Faculty Development programs and innovative instructional designs centered on human-machine collaboration and aimed to foster higher-order cognitive skills, ethical-intellectual virtues, and a flexible mindset.
Saadaoui Marouane1
The rapid integration of artificial intelligence (AI) into educational systems worldwide has opened new avenues for transforming teaching practices, learning environments, and institutional performance. This study investigates the impact impact of AI adoption and pedagogical innovation on the efficiency and quality of the Moroccan educational system . Drawing on an interdisciplinary framework combining educational sciences, economics of education and digital sociology , this research addresses three core questions : How are AI- based tools currently being adopted in Moroccan educational institutions ? To what extent does pedagogical innovation mediate the relationship between AI integration and educational outcomes ? And what structural,cultural, and institutional barriers constrain this transformation in a developing-country context ? Using a mixed-methods approach including a systematic literature review, comparative analysis of MENA educatonal policies, and a field survey targeting students and faculty at Sultan Moulay Slimane University , the study identifies critical succes factors and persistent obstacles to effective AI integration , Findings highlight significant disparities in digital infrastructure, uneven teacher digital literacy, and the absence of a coherent national AI in education strategy as key limiting factors . This paper contibutes to the growing scholarship on AI driven educational reform in the Global South and offers evidence-based policy recommendations for Moroccan decision-makers, educators and academic institutions commited to buildin a more equitable, innovative and performance-oriented educational system .
An investigation of EFL teachers' attitudes
Aoula Cherbal1
The artificial intelligence (AI) rapid proliferation has affected the realm of education ushering in radical transformations and reshaping instructional demands. In a multilingual setting, AI literacy has emerged as an essential competency. It enables teachers to effectively utilize technological resources and enhance their professional skills as it supports innovative teaching practices, personalized learning, and improved classroom engagement. Yet, many teachers often fail to interact meaningfully outside the boundaries of their conventional methods and encounter difficulties in applying AI tools. This empirical study aims to investigate university teachers' attitudes towards the effectiveness of AI literacy on the enhancement of their teaching skills and professional growth. A mixed-methods employed a sequential exploratory design was adopted. Quantitative data were collected through a questionnaire administered to 50 EFL teachers from the Department of English at the University of Jijel. To gain deeper insights into participants' perceptions and experiences, qualitative data were gathered through semi-structured interviews. The quantitative data were analyzed using descriptive statistics, while the qualitative data were examined through thematic analysis. The findings reveal that teachers generally held positive attitudes towards AI literacy and recognize its potential to improve instructional planning, classroom management, assessment practices, and professional decision-making. Classrooms led by AI trained teachers were perceived as inclusive, culturally sensitive, and reveal students' engagement. This study addressed the human element by showing how AI literacy is perceived as central for students' engagement and teachers' agency. It contributes to pedagogy by providing insights about the challenges encountered and the importance of innovative pedagogical practices for classroom interaction and teacher professional development programs. It informs policy- makers how teachers' training in AI can bring technical skills and empowerment when appropriately applied in multicultural contexts.
A perspective
Nagendra Kumar1; Mahesh Krishna2
Artificial intelligence in the Global South raises a question that is also a provocation: who builds knowledge, and under what material conditions? India sits somewhat awkwardly in this picture. Its institutional ambitions are real (the India AI Mission's cabinet-approved ₹10,372 crore outlay, AICTE's declaration of 2025 as the Year of Artificial Intelligence across 14,000+ institutions), but ambition and structural position are different things, and India's structural position remains that of a country whose AI ecosystem depends heavily on tools built elsewhere, for other people. This paper examines AI adoption in Indian higher education through that lens. Drawing on NEP 2020, the India AI Mission, AICTE mandates, and recent survey data on institutional uptake, it argues that India's current trajectory reproduces existing hierarchies of knowledge production. The dependence on anglophone platforms (OpenAI, Google, Microsoft) trained on data that skews toward Western epistemological norms is the clearest instance of this. An AI infrastructure that cannot adequately process Hindi, Tamil, or Bengali is doing something more consequential than failing a usability test; it is, in Santos's terms, a vector for epistemicide. The paper also takes seriously the material conditions that underpin this. The digital divide between urban and Tier 2/3 institutions, the documented unpreparedness of faculty (only 17% qualify as advanced AI users), and the data poverty of Indian HEIs are not incidental frictions in an otherwise sound policy framework. They reflect a deeper problem: that India's AI ambitions are being pursued within an architecture never designed with the Global South in mind. The paper ends with a proposal for what equity-first, epistemically sovereign AI policy in Indian higher education would actually require, with Indic-language model development and community-grounded data governance at its centre.
Ethical judgment and professional agency in higher education
Sondes Tamer1
The rapid integration of Generative Artificial Intelligence (GenAI) into higher education has fundamentally disrupted traditional pedagogical models, necessitating a profound re-evaluation of teaching competence. This article explores how the shift from knowledge transmission to AI- mediated learning environments redefines the professional role of the educator. It argues that technical proficiency in AI is insufficient; rather, the core of modern teaching competence lies in the exercise of ethical judgment and the assertion of professional agency. It aims to highlight the transition of teachers/educators from content providers and instructors to "learning architects and critical evaluators" of AI generated content. Adopting a descriptive approach, data were collected through interviews, and questionnaires distributed to 15 EFL teachers of Chadli Benjedid University (CBU) El Tarf to gain deeper understanding to their experiences. Findings suggest that the future of higher education depends on the ability of teachers to reclaim and empower their agency as critical curators of technology, ensuring that AI enhances rather than replaces the essential human dimension of the teaching-learning relationship.
A systematic literature review
Malak Gattaf1
The rapid advancement of artificial intelligence (AI), particularly generative AI and large language models, has transformed higher education and heightened the need for English as a Foreign Language (EFL) teachers to develop AI literacy. AI literacy encompasses the knowledge, skills, attitudes, and ethical awareness required to critically understand, evaluate, and responsibly integrate AI technologies into teaching and learning practices (Long & Magerko, 2020; Ng et al., 2021). In higher EFL education, AI-powered tools are increasingly employed for lesson planning, content generation, assessment, feedback, and personalized learning, underscoring the importance of preparing Algerian EFL teachers to effectively navigate AI-mediated learning environments. This paper presents an ongoing systematic review of teacher AI literacy in higher EFL education, with particular attention to its implications for Algerian higher education. Guided by the PRISMA framework, relevant studies are being identified through major academic databases and analyzed thematically. The review focuses on conceptualizations of AI literacy, key competencies required for EFL teachers, teachers' perceptions and readiness, professional development initiatives, and challenges associated with AI integration. Preliminary findings suggest that EFL teachers generally recognize the pedagogical potential of AI technologies and their capacity to enhance teaching practices and learner engagement (Kasneci et al., 2023). However, emerging evidence points to persistent challenges related to limited AI competencies, insufficient training opportunities, ethical concerns, and the lack of discipline-specific AI literacy frameworks. By mapping current research trends and identifying gaps in the literature, this review seeks to provide insights for teacher education, institutional policies, and professional development programs aimed at fostering the responsible and effective integration of AI in Algerian higher education.
A systematic review of teacher AI literacy in higher education
Malak Gattaf1
The rapid advancement of artificial intelligence (AI), particularly generative AI and large language models, has transformed higher education and heightened the need for English as a Foreign Language (EFL) teachers to develop AI literacy. AI literacy encompasses the knowledge, skills, attitudes, and ethical awareness required to critically understand, evaluate, and responsibly integrate AI technologies into teaching and learning practices (Long & Magerko, 2020; Ng et al., 2021). In higher EFL education, AI-powered tools are increasingly employed for lesson planning, content generation, assessment, feedback, and personalized learning, underscoring the importance of preparing Algerian EFL teachers to effectively navigate AI-mediated learning environments. This paper presents an ongoing systematic review of teacher AI literacy in higher EFL education, with particular attention to its implications for Algerian higher education. Guided by the PRISMA framework, relevant studies are being identified through major academic databases and analyzed thematically. The review focuses on conceptualizations of AI literacy, key competencies required for EFL teachers, teachers' perceptions and readiness, professional development initiatives, and challenges associated with AI integration. Preliminary findings suggest that EFL teachers generally recognize the pedagogical potential of AI technologies and their capacity to enhance teaching practices and learner engagement (Kasneci et al., 2023). However, emerging evidence points to persistent challenges related to limited AI competencies, insufficient training opportunities, ethical concerns, and the lack of discipline-specific AI literacy frameworks. By mapping current research trends and identifying gaps in the literature, this review seeks to provide insights for teacher education, institutional policies, and professional development programs aimed at fostering the responsible and effective integration of AI in Algerian higher education.
Reem Khalouk Issa1
This study is concerned with ELT in higher education. It aims at examining the issue of how we can achieve more effective teaching/learning in the field of English language teaching through exploring teachers' and students' perceptions. The study presents a comparative analysis of students' and teachers' perceptions regarding effective teaching in the context of Aleppo University- specifically at the Higher Institute of Languages. It employs semi-structured interviews with six teachers and six students. The findings show that students perceive effective teaching as teaching in a clear and easy language; they highlight similarities and differences in students' and teachers' perceptions. The findings also refer to the factors that influence such perceptions such as educational and personal factors. The findings suggest and recommend diverse teaching strategies and techniques to be used in the field of ELT. They also yield practical implications for teachers and teacher trainers.
N. Raghavendra Rao1
The basic functions, such as production, marketing, and finance, have remained the same as the world moved from an agricultural economy to an industrial economy. Business processes, procedures, methods, strategy, management thinking, and approaches related to the basic functions have been changing. Any change, such as adding new features to existing products, designing a new product, changing business processes, the business model, and other activities within the basic functions, has to be carried out. This assignment is given to the research and development department in an enterprise. The role of research and development in an enterprise is to think of a new approach for the above activities. If there is no research and development within an enterprise, a group of executives, at the instruction of top management, will form a team to implement an innovative approach for the above activities. Their innovative approach will be based on their professional experience and knowledge. Generally, their approach will aim to be competitive in the market. In the present business scenario, it is not enough for an enterprise to be competitive. It is required to be acceptable as a supplier or service provider. There is a need for a knowledge-based system to be created in an enterprise. Many intelligent software tools are required to make use of this system. These tools have only the specific features needed to use the data in this system. The concept of artificial intelligence enables the creation of the specific required features for an enterprise.
Opportunities, ethical challenges, and the future of higher education
Sukhmeet Kaur1
The rapid adoption of Generative Artificial Intelligence (GenAI) tools, such as ChatGPT, Claudie, and other LLM Models, is shaping higher education worldwide, including in India. At the same time, the rigorous use of AI raises concerns for the ethical use of AI and its future challenges. This conceptual paper reviews recent developments in the literature on Gen AI in Indian higher education and explores their implications in the context of the National Education Policy (NEP) 2020. The paper discusses the opportunities AI offers for personalized learning, improved academic support, and enhanced student engagement, while also highlighting challenges related to ethical use, overdependence on AI-generated content, and the digital divide. The paper concludes that a balanced and human-centered approach can help Indian universities harness the benefits of AI while preserving academic integrity and educational quality. The study contributes to the ongoing discussion on AI-enabled education and offers recommendations for policymakers, educators, and students in the Indian higher education context.
Artificial intelligence, information integrity and social responsibility in higher education
Edmont Pasipamire1
Artificial Intelligence (AI) is changing the way knowledge is created, accessed and evaluated in higher education. While artificial intelligence provides opportunities for learning and research, it also poses challenges in terms of misinformation, fake content, algorithmic bias, intellectual property and the responsible use of information generated by AI. This paper presents a scope review of research in the fields of artificial intelligence, academic libraries, information integrity and social responsibility in higher education. The review systematically maps existing literature to identify the main themes, emerging concerns and gaps in the knowledge on the role of libraries in the information environment mediated by artificial intelligence. Special attention is paid to AI literacy, critical information assessment, information integrity, ethical use of artificial intelligence and the evolving responsibilities of librarians. The paper conceptualises libraries as compasses in an information landscape mediated by artificial intelligence, helping learners and researchers navigate an increasingly complex information environment while maintaining critical judgment and ethical awareness. The review shows how the role of academic libraries extends beyond traditional access to information to promote responsible use of knowledge produced by artificial intelligence. Linking libraries and information science to AI-related discussions in education and society, the paper highlights the importance of information integrity and human-centred training in developing responsible AI-enabled learning environments.
Artificial Intelligence, Governance and the Future of Quality Assurance in the Global South
Dervin K. Cochrane1
Can artificial intelligence transform accreditation in the Global South from a periodic, compliance-driven exercise into a more transparent, continuous and integrity-focused system of higher-education governance? This paper addresses this question by examining the emerging role of artificial intelligence (AI) and digital technologies in accreditation against the structural vulnerabilities that characterize quality-assurance systems in Jamaica, the Caribbean and the wider Global South. Drawing on my recent broader interdisciplinary book chapter on accreditation, governance and AI, the paper argues that accreditation should not be understood merely as a technical mechanism for verifying institutional quality. It also operates as a system of governance, legitimacy and power. Yet its dependence on periodic reviews, institutional self-reporting, documentary evidence and centralized decision-making can create opportunities for symbolic compliance, data manipulation, regulatory capture and other forms of institutional gaming. These vulnerabilities are conceptualized through the “Accreditation Wall”: the assumption that formal accreditation procedures constitute an effective barrier against institutional failure and misconduct, even when actors may circumvent the safeguards themselves. The paper argues that Artificial Intelligence (AI) creates an opportunity to reconceptualize this model. AI-assisted anomaly detection, continuous data analysis, digital credential verification and real-time monitoring could enable accreditation systems to move beyond episodic procedural compliance towards more dynamic forms of institutional accountability. For resource-constrained systems in the Global South, these technologies could potentially strengthen regulatory capacity while improving transparency and the early detection of irregularities. However, technological adoption does not automatically produce better governance. Artificial Intelligence (AI) may reproduce existing inequalities and introduce new problems involving algorithmic bias, data governance, technological dependency, institutional capacity and accountability for automated decisions. The paper therefore rejects both technological determinism and the assumption that Artificial Intelligence (AI) can substitute for professional judgement. It concludes that the central question for the Global South is not simply whether AI should be incorporated into accreditation, but how AI can be governed so that it strengthens rather than reproduces the weaknesses of existing accreditation systems? A future accreditation architecture should consequently combine technological innovation with human oversight, transparency, contextual relevance, institutional integrity and social justice.
A Responsible Management Perspective
Hubert Gąsiński
The growing integration of artificial intelligence into the banking sector is transforming work organisation, managerial decision-making and employee responsibilities. Although AI can improve operational efficiency, support decision-making and facilitate knowledge management, its implementation may also generate new occupational challenges, including technological stress, uncertainty about professional roles, reduced autonomy and concerns about job security.
This paper aims to examine the relationship between AI-driven digital transformation, employee well-being and occupational stress in the banking sector, with particular attention to the role of responsible management practices. Building on research on employee well-being, organisational climate, organisational agility and the use of information and communication technologies, the study will explore how managerial support, employee participation, digital competencies and transparent communication may influence employees’ responses to AI adoption.
The paper will develop an analytical framework linking AI implementation, organisational practices and employee outcomes. An integrative review of the relevant literature will provide the basis for identifying key relationships and formulating propositions for future empirical research.
The study seeks to contribute to the literature on responsible digital transformation by highlighting the importance of balancing technological innovation with employee well-being, professional autonomy and sustainable organisational development.
Georgios Alexandropoulos
This project presents an AI-assisted educational digital game designed to support the teaching and learning of the Neolithic Age through an interactive and experiential environment. The game was developed using AI tools, including ChatGPT, to assist in content creation, question design, narrative structuring, and educational scaffolding aligned with pedagogical objectives. The virtual environment places students in a Neolithic village where they explore different areas such as agricultural fields, animal domestication zones, tool-making spaces, and communal areas. Through interaction with these spaces, learners answer multiple-choice questions related to key Neolithic developments, including early agriculture, domestication of animals, technological innovation, and social organization. The design is grounded in constructivist learning theory (Piaget), multimodal learning approaches (Cope & Kalantzis), and supported by elements of behaviorism, cognitivism, and social learning theory (Vygotsky). Gamification strategies such as immediate feedback, scoring systems, and progressive cognitive challenge are integrated to enhance motivation, engagement, and knowledge retention. The instructional structure also draws on established educational frameworks, including Bloom’s Taxonomy, Gagné’s Nine Events of Instruction, and the ADDIE model, ensuring systematic alignment between learning objectives and game mechanics. The game encourages active learning by requiring students to explore, interpret, and apply knowledge rather than rely on memorization. By combining historical content with interactive gameplay and AI-supported design, the project promotes critical thinking, digital literacy, and deeper conceptual understanding of Neolithic history. Overall, the educational game demonstrates how artificial intelligence can enhance instructional design and contribute to the development of engaging, student-centered learning environments.