Human-Guided Agentic AI for Skill Formation in Indian Higher Education: A Framework-Centred Study for Learner Modelling, Skill-Gap Diagnosis and Responsible Evaluation.

Saved in:

Bibliographic Details
Title:	Human-Guided Agentic AI for Skill Formation in Indian Higher Education: A Framework-Centred Study for Learner Modelling, Skill-Gap Diagnosis and Responsible Evaluation.
Authors:	Manjunath, D. R.¹ manjunthdr.cse@bmsce.ac.in, Preetha, S.¹ preetha.ise@bmsce.ac.in, Kumar, B. Anil² anilkumarb.rvitm@rvei.edu.in, Sumanth Reddy, Siva³ sumanthdsatm@gmail.com, Krupa, K. S.¹ krupaks.csi@bmsce.ac.in
Source:	International Journal of Special Education. 2026 Special Issue, Vol. 41, p1123-1140. 18p.
Subject Terms:	Outcome-based education, Postsecondary education, Educational evaluation, Intelligent tutoring systems, *Learning analytics, Intelligent agents, Diligence, Data mining
Abstract:	Artificial intelligence in education has a longer research history than the current wave of large language models. Intelligent tutoring systems, knowledge tracing, learning analytics and educational data mining established the methodological basis for adaptive support; generative AI subsequently made natural-language feedback, question generation, coding assistance and assessment redesign widely accessible; and agentic AI now promises multi-step workflows that can plan, retrieve, monitor and adapt. The difficulty is that agentic educational AI is advancing faster as a technology than as a validated classroom practice, and controlled evidence is especially scarce in Indian higher and engineering education, where scale, multilingual learners, Outcome-Based Education (OBE) requirements and data-protection duties shape what responsible deployment can look like. This paper addresses that gap through design-science reasoning: it synthesises foundational and recent evidence only to the extent needed to derive design requirements, and then develops SAKSHAM-AI, a humanguided agentic framework built on the principle of bounded autonomy. The framework integrates learner profiling, skill-gap diagnosis, knowledge tracing, retrieval-augmented content grounding, formative assessment, misconception-level feedback, self-regulated learning support, faculty dashboards, CO/PO outcome mapping and governance monitoring into a single auditable workflow in which agents recommend, retrieve, explain and flag while teachers retain authority over high-stakes academic decisions. We specify a trial-ready evaluation protocol-a 12–16-week pilot with a delayed-retention test-reporting knowledge gain, skill-gap closure, retention, independent transfer, equity, hallucination rate, explainability, privacy minimisation, escalation and OBE coverage. The contribution is therefore a bounded-autonomy, human-guided architecture together with its measurement and governance design, rather than a deployment claim; the framework is proposed and trial-ready but requires empirical validation in live institutional settings before any effectiveness claim can be made. [ABSTRACT FROM AUTHOR]
	Copyright of International Journal of Special Education is the property of International Journal of Special Education and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database:	Education Research Complete

Description
Abstract:	Artificial intelligence in education has a longer research history than the current wave of large language models. Intelligent tutoring systems, knowledge tracing, learning analytics and educational data mining established the methodological basis for adaptive support; generative AI subsequently made natural-language feedback, question generation, coding assistance and assessment redesign widely accessible; and agentic AI now promises multi-step workflows that can plan, retrieve, monitor and adapt. The difficulty is that agentic educational AI is advancing faster as a technology than as a validated classroom practice, and controlled evidence is especially scarce in Indian higher and engineering education, where scale, multilingual learners, Outcome-Based Education (OBE) requirements and data-protection duties shape what responsible deployment can look like. This paper addresses that gap through design-science reasoning: it synthesises foundational and recent evidence only to the extent needed to derive design requirements, and then develops SAKSHAM-AI, a humanguided agentic framework built on the principle of bounded autonomy. The framework integrates learner profiling, skill-gap diagnosis, knowledge tracing, retrieval-augmented content grounding, formative assessment, misconception-level feedback, self-regulated learning support, faculty dashboards, CO/PO outcome mapping and governance monitoring into a single auditable workflow in which agents recommend, retrieve, explain and flag while teachers retain authority over high-stakes academic decisions. We specify a trial-ready evaluation protocol-a 12–16-week pilot with a delayed-retention test-reporting knowledge gain, skill-gap closure, retention, independent transfer, equity, hallucination rate, explainability, privacy minimisation, escalation and OBE coverage. The contribution is therefore a bounded-autonomy, human-guided architecture together with its measurement and governance design, rather than a deployment claim; the framework is proposed and trial-ready but requires empirical validation in live institutional settings before any effectiveness claim can be made. [ABSTRACT FROM AUTHOR]
ISSN:	08273383