AI and Andragogy

This week I would like to share a book chapter and a research article focused on the concept of Andragogy in the age of AI. The first article is entitled, “Andragogy in the Age of AI: Transformative Pathways for Adult Education” by Storey and Wagner (2024). The second article is “GenAI-Infused Adult Learning in the Digital Era: A Conceptual Framework for Higher Education” by Adarkwah (2024). 

As a preface/reminder, Malcolm Knowles (1970, 1980, 1984) proposed andragogy as a theory and practice of adult learning, distinguishing it from pedagogy (child-focused instruction). His model identifies six core assumptions about adult learners:

1. Need to Know - Adults must understand why they need to learn something before investing effort.

2. Self-Concept - Adults see themselves as autonomous and self-directed.

3. Prior Experience - Adults bring rich experiences that serve as learning resources.

4. Readiness to Learn - Adults are ready to learn things they perceive as relevant to real-life tasks.

5. Orientation to Learning-  Adults prefer problem-centered learning applicable to life.

6. Motivation to Learn - Internal motivators (job satisfaction) are more powerful than external ones.

Story and Wagner (2024) remind us of the foundational principles of adult learning (Knowles, 1984 andragogy) emphasizes the importance of self-direction, drawing from past experiences, relevance to real-life situations, and a problem-solving approach (Jeffries & Ahn, 2024). Adult learning through active participation, subject relevance to working lives, and building upon existing experiences align with learning science principles, such as constructivism, cognitive load theory, and the importance of meaningful context in learning (Wang et al., 2023). This book chapter also shares a brief history of Andragogy and early AI, which includes OLI, ALEKS, Cognitive Tutor, and CENTURY Tech, as well as Augmented Reality and Virtual Reality.

The chapter concludes that in the near future there will be an enhanced Shift Towards Self-Directed Learning; Incorporation of Active, Relevant Learning Experiences; Facilitation of Learning; Experiential Learning; Collaborative Learning; Continuous Learning and Adaptation; and a Shifting andragogy. The authors state that an “active dialogue between AI designers and educators, must be developed to ensure that innovative AI technology is grounded on effective andragogical principles and AI-based learning science.

Whereas Adarkwah (2024) proposes a “GenAI adult learning ecology” framework (GenAI-ALE) for higher education institutions in this digital era. The GenAI-ALE considers eight essential principles categorized into two main factors; 

• institutional (AI curriculum design, AI divide, AI policy, AI ethics) and 

• interpersonal (AI human-centered andragogy, AI literacy, AI interest, AI virtual learning). 

Knowles’ (1984) andragogical model is used to provide a context for integrating AI into adult learning. Applying the framework in a real-world context follows four iterative systematic steps; 

1. pre-perception and perception, 

2. AI readiness, 

3. Assessment, and 

4. Outcome. 

The author shares specific examples of using AI for andragogy:

Examples	Application in Adult Learning	Authors
ChatGPT	Improving the reading, writing, critical thinking skills, and self-directed learning	(Lin, 2023)
Synthesia	Generating instructional video content	(Leiker et al., 2023)
Midjourney	Creating realistic images to enhance the immersive learning experience	(Hsu & Ching, 2023a; Sætra, 2023)
Dall-E 2	Automating assessment and enhancing the creativity	(Cacicio & Riggs, 2023)
InstructGPT	Creating customized learning materials	(Bhavya et al., 2022)
Perplexity	Providing a “knowledge hub”  tailored to the needs	(UNESCO, 2023b)

The author provides a comparison of traditional Adult Learning Methods with AI-Infused Learning Approaches.

Aspect	Traditional Methods	AI-Infused Methods	Sources
Delivery and Facilitation	Face-to-face seminars, and workshops	The use of digital resources tailored to learner needs	(Dwivedi et al., 2023; OECD, 2023)
Assessment	Paper-based exams as a summative assessment	Multiple form of summative or formative assessment such as digital with instant analysis and visual representation	(Baidoo-Anu & Owusu Ansah, 2023; Dwivedi et al., 2023)
Learning Activities and Feedback	Course materials with passive, teacher- centered learning, and manual feedback	Dynamic course materials with inquiry-based learning and personalized feedback	(Adarkwah, Ying, et al., 2023; Lund & Wang, 2023; Salinas-Navarro et al., 2024)
Learning Engagement	Teacher-student or peer-to-peer classroom interactions	Immersive and simulated learning experience	(Baidoo-Anu, 2023; Mishra, 2023; Salinas-Navarro, 2024)
Accessibility	Fixed time and space with limited access to materials outside the classroom	Technology-enhanced learning environment with on-demand access to course materials	(Chiu, 2023; Tlili et al., 2023)

In summary, Knowles’ andragogy aligns closely with constructivist theories, as both emphasize building new knowledge upon adults’ prior experiences—an approach strengthened by AI through adaptive content and personalized learning pathways. Humanistic theories resonate with Knowles’ emphasis on self-direction and intrinsic motivation, which AI supports by offering autonomy-enhancing tools such as progress dashboards and customized assessments. Experiential learning is also reflected in andragogy’s focus on problem-centered tasks, with AI enabling immersive simulations, real-world scenario generation, and reflective prompts. Transformative learning complements andragogy’s respect for learners’ perspectives by using AI-facilitated journaling, feedback tools, and dialogue agents to foster critical reflection. Situated and authentic learning theories underscore the need for contextual relevance in adult education, which AI addresses by generating discipline-specific case studies, virtual role-play, and contextualized datasets. Lastly, information processing theory aligns with andragogy’s concern for meaningful learning organization, as AI enhances cognitive support through smart note-taking, retrieval practice, and personalized content chunking. Collectively, these synergies suggest that AI, when grounded in adult learning theory, can create responsive, personalized, and contextually rich educational experiences that support andragogical goals.

References

Storey, V. A., & Wagner, A. (2024). Andragogy in the age of AI: Transformative pathways for adult education. In V. Wang (Ed.). Integrating AI into pedagogical and andragogical education. IGI Global. DOI: 10.4018/979-8-3693-6130-6 

Adarkwah, M. A. (2024). GenAI-Infused adult learning in the digital era: A conceptual framework for higher education. Adult Learning, 0(0). https://doi.org/10.1177/10451595241271161

Knowles, M. S. (1984). The adult learner: A neglected species (3rd ed.). Houston, TX: Gulf Publishing.

Knowles, M. S. (1980). The modern practice of adult education: From pedagogy to andragogy (Rev. ed.). Englewood Cliffs, NJ: Cambridge Adult Education.

Atkinson, R. C., & Shiffrin, R. M. (1968). Human memory: A proposed system and its control processes. In K. W. Spence & J. T. Spence (Eds.), The psychology of learning and motivation (Vol. 2, pp. 89–195). Academic Press.

Bruner, J. S. (1960). The process of education. Harvard University Press.

Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. Springer.

Kolb, D. A. (1984). Experiential learning: Experience as the source of learning and development. Prentice Hall.

Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Cambridge University Press.

Mezirow, J. (1991). Transformative dimensions of adult learning. Jossey-Bass.

Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press.