Volume: 3 Issue: Special Issue 1
Year: 2026, Page: 1-6, Doi: https://doi.org/10.70372/jeltp.v3.sp1.1
KARE–Teaching Learning Centre has operationalized a human-centric, tool-agnostic microteaching assisted by Artificial intelligence (MT–AI) model that breaks core topics into 10–15 minute micro-segments, couples AI-assisted preparation with visual pedagogy, and closes each loop with synchronous formative checks and asynchronous application tasks mapped to CO–PO–PSO and X-components for applied learning. Faculty leverage a common template specifying split topic, justification, activity, AI tools, synchronicity, roles, and assessment hooks, while students engage via quizzes, simulations, coding/ML notebooks, and data-to-insight workflows with transparent AI attribution and LMS evidence trails for auditability. The model scales across Heat Transfer, Bioenergy, Operating Systems, Data Structures, Business Economics, Java Programming, Biomedical Sensors, Physics, Civil Engineering courses, and Statistics, using domain-appropriate stacks such as Perplexity/NotebookLM for retrieval, Napkin/Pictory for visuals, Socrative/Wayground/Kahoot/Forms for analytics, Python/Weka/Excel-ML for ML, and ANSYS/CFD/solvers for engineering simulations. Early evidence indicates higher clarity, improved quiz mastery, and strong sessional outcomes in cohorts taught with MT–AI.
Keywords: AI-assisted microteaching; Applied learning analytics; Microteaching framework; Pedagogical mapping
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