Shashidhar Venkatesh Murthy1* and Ramnarayan Komattil2
Affiliation:
1Associate Professor and Head of Pathology, College of Medicine & Dentistry, James Cook
University, Australia
2Manipal Academy of Higher Education, Manipal, India.
*Corresponding author:
Shashidhar Venkatesh Murthy, Associate Professor and Head of Pathology, College of Medicine & Dentistry, James Cook University, Australia
Received: March 16, 2026;Accepted: March 24, 2026; Published: May 06, 2026
Medical Education
Letter to the editor articles
Pedagogy, andragogy, and heutagogy describe a useful con- tinuum of learner autonomy, with heutagogy emphasising self-de- termined learning in technology-rich environments [1,2]. However, rapid artificial intelligence (AI) integration into medical curricula suggests a distinct paradigm.
Recent AI applications—adaptive platforms, generative tutors, virtual patients—drive personalisation and efficiency but raise bias, transparency and over-reliance concerns [3]. An AI-gogy framework helps educators articulate the learner-AI co-agency format, design AI literacy outcomes, and anticipate ethical and assessment implica- tions.
Anchoring AI-gogy alongside established paradigms supports deliberate curriculum design, scholarship and governance of AI-me- diated medical education. Table 1 compares the paradigms and how AI-gogy fits in as next level.
We propound the term AI-gogy to describe AI-enabled self-de- termined learning where human learners and AI share co-agency in goals, pathways and reflection. AI curates adaptive cases, generates tailored assessments and provides formative feedback, while learn- ers critically appraise outputs. This embeds dynamic human-AI partnership at the core of metacognitive learning, an extension of heutagogy [1,3].
Funding: We authors confirm that we have not received any funding for this work.
AI use: We authors confirm that we have taken help of AI “Per- plexity pro” version to polish the language & style at the final edit phase only. AI has not been used in any other part of this work. The central concept is original idea came while authors were discussing plan for upcoming medical education workshop.
Table 1:
|
Dimension |
Pedagogy |
Andragogy |
Heutagogy |
AI-gogy* |
|
Core principle |
Teacher-centred, content-driven instruction & the learner is largely dependent. |
Participatory learning where teacher sets the curriculum & objectives and adults learn by their choice of methods. |
Learner- driven self-determined learning based on professional challenges, capability, reflection and problem solving. Teacher only guides when needed. |
Human-AI co-agency in learning; AI systems dynamically personalise teaching based on student performance. Teacher guides when AI misses. |
|
Rationale |
Assumes learners have limited prior knowledge and they require structured guidance, clear objectives and external motivation. |
Recognises that adults bring prior experience, prefer autonomy, and practice-linked learning. |
Uses learners’ internal motivation, adaptability, self-regulation and lifelong learning beyond fixed curricula. |
Interactive AI system responds & adapts to student learning. Shared decision-making with intelligent systems. |
|
Typical application in medical education |
Large-group lectures on basic sciences or teacher-controlled e‑modules with fixed sequences. |
Case or problem-based discussions linked to clinical practice where the learning method is chosen by the learner. |
Self-determined projects, flexible online pathways where learners have full control over their learning. |
Adaptive AI agents that co-construct learning based on a predetermined objective using meta‑cognitive reflection. |
*AI-gogy shown as a proposed, emergent category extending heutagogy into AI-mediated, co-agentic learning.