Biography
Dr. Pratheesh Abraham serves as an Associate Professor in English Education at St. Thomas College of Teacher Education, Pala, Kerala, India. He holds a Ph.D. in education from Mahatma Gandhi University, Kottayam, Kerala, India. With two decades of experience in teacher education across various institutions, he has established himself as a dedicated educator committed to advancing English language pedagogy. His research interests include methodology for English education, pedagogical analysis in English, educational psychology, guidance and counselling, and communicative English approaches. Through his research and teaching, he continues to make meaningful contributions to the field of English education and teacher development. He is the chief editor of the journal Educational Extracts.
Conference contributions 2
AI-SUPPORTED TRANSFORMATIVE LESSON MODEL FOR DEVELOPING MATHEMATICAL MODELLING SKILLS IN SECONDARY STUDENTS
Mathematical modelling is a cognitively demanding and educationally vital competency in secondary mathematics, requiring learners to translate complex real-world situations into structured mathematical forms and critically interpret their solutions. Conventional classrooms, however, rarely provide the reflective, disorienting, and meaning-making conditions that genuine modelling development requires. This paper introduces the AI-Supported Transformative Lesson Model (ATLM), a novel pedagogic framework that integrates Artificial Intelligence tools within teaching design, lesson planning, and feedback practices, grounded explicitly in Jack Mezirow's Transformative Learning Theory (TLT). The ATLM involves five pedagogic phases: Disorientation, Critical Reflection, Discourse, Reframing, and Integration, all supported through AI tools that scaffold, challenge, and respond to student thinking in real-time. AI-powered lesson planners design tasks to deliberately surface cognitive conflict, while intelligent scaffolding agents help students to critically review their assumptions. AI-driven formative feedback systems also provide tailored, process-level feedback to support reflective engagement with each stage of the mathematical modelling cycle (problem formulation, abstraction, representation, validation, and interpretation). The paper ends with the proposal of the AI-Supported Transformative Lesson Model, a conceptual framework that maps the intersection between AI-supported pedagogy and Transformative Learning Theory phases for building mathematical modelling skills. This model contributes to the broader conversation on transformative, subject-specific AI integration in secondary education, and offers a potential basis for future empirical validation and classroom application.
BRIDGING BARRIERS:
AI as a catalyst for inclusive education - universal design for learning, accessibility, and special needs support
The growing acceptance of Artificial Intelligence (AI) technology across educational settings offers a transformative but varied possibility to strengthen inclusive education. Despite decades of legislative frameworks and policy campaigning, including the United Nations Convention on the Rights of Persons with Disabilities (CRPD)(2006) and different acts all over the world, fundamental hurdles to equitable learning remain in everyday classrooms. The current study explores how the combination of AI- driven pedagogical tools and the Universal Design for Learning (UDL) might assist in eliminating structural exclusions in formal educational institutions. The study provides a formal evaluation of the present systematic review (2018-2026) employing the OpenAlex and Web of Science databases from Schools and universities in both the Global North and South. The study focuses on how AI applications, including adaptive learning systems, intelligent tutoring systems (ITS), computer vision for sign language, and emotionally responsive agents. Findings disclose that the AI technologies visibly enhance accessibility, especially for learners with visual impairments, dyslexia, autism spectrum conditions, and hearing disabilities. The paper concludes by proposing the Responsible AI-UDL Integration Model (RAIM) that focuses on disabled learners and educators. The study contributes to the growing scholarship on AI ethics in education that disability justice is an indispensable dimension of socially responsible AI.