Journal of Effective Teaching and Learning Practices

Volume: 3 Issue: Special Issue 1

  • Open Access
  • Original Article

Teaching, Testing, Transforming: A Multi Faculty Experience with AI-Assisted Microteaching

1Naresh Kumar Sharma, 2Srinivasan Durairaj, 3Narayanan S, 4Jenyfal Sampson

1,4 Kalasalingam Teaching Learning Centre (KAL TEC), Kalasalingam Academy of Research and Education, Tamil Nadu 626126, India. [email protected], [email protected] 
2 Professor, Mathematics & Biological Sciences Division, Richland Community College, One College Park, Decatur, IL – 62521. [email protected] 
3 Vice Chancellor, Kalasalingam Academy of Research and Education, Tamil Nadu 626126, India. [email protected]

Year: 2026, Page: 1-6, Doi: https://doi.org/10.70372/jeltp.v3.sp1.1

Abstract

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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Cite this article

Naresh Kumar Sharma, Srinivasan Durairaj, Narayanan S, Jenyfal Sampson. Teaching, Testing, Transforming: A Multi Faculty Experience with AI-Assisted Microteaching. Journal of Effective Teaching and Learning Practices. 2026;3(Sp1):1-6

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