Empowering Educators: Operationalizing Age-Old Learning Principles Using AI

Synthesis: Empowering Educators: Operationalizing Age-Old Learning Principles Using AI — A theoretical framework helping educators navigate AI integration while preserving the core principles of effective teaching and learning. The authors examine Dewey's experiential learning, situated cognition, and distributed cognition through four lenses (inputs, methods, conditions, outcomes), showing how AI operationalizes each at scale, and argue AI is a tool that enhances rather than replaces the educator's role. Grounded in health professions education at the Medical University of South Carolina (MUSC), the paper also offers the Four-Step AI Response Continuum framework (ignore, address, redesign, redefine) to meet educators at their varying levels of readiness.

Julaine Fowlin, Denzil Coleman, Shane Ryan, Carina Gallo, Elza Soares & NiAsia Hazelton — Education Sciences, 15(3), 393.

Key Findings

The paper opens from a Constructivism foundation (Richey et al., 2011): learning results from a personal interpretation of an experience, is an active process in realistic and relevant situations, and results from exploring multiple perspectives. On this basis it analyzes three age-old learning principles — each through the lenses of inputs, methods, conditions, and outcomes — and demonstrates how AI operationalizes them:

• Dewey's experiential learning (realism, interaction, continuity) uses active, project-based methods and hands-on experimentation. AI operationalizes it through adaptive learning pathways, AI-driven immersive AR/VR environments, and Generative AI prompts that create responsive, personalized learning experiences. In an Environmental Science example, students manipulate variables in a virtual coastal-ecosystem Simulation, interact with AI-powered virtual stakeholders, and collaborate in virtual field studies, while AI data analytics guide educators' personalized interventions.

• Situated cognition (authenticity, social interaction, cultural embeddedness) treats knowing and doing as inseparable, with learning tied to context and culture. AI operationalizes it by generating immersive environments that simulate real-world situations, using virtual agents (chatbots/LLMs) via natural language processing to act as experts coaching apprentices. In a business mergers-and-acquisitions example, students negotiate with AI-simulated stakeholders and communities of practice, enabling cognitive apprenticeship at scale. Cognition here is also recognized as embodied — physical experience shapes thinking and learning.

• Distributed cognition (DCog, from Hutchins) holds that cognitive processes are distributed across people, tools, and environments rather than confined to individual minds. AI acts as a cognitive partner within these systems. The paper recommends a two-phase approach to avoid automation-related deskilling: Phase 1 builds core competencies without AI, then Phase 2 introduces AI as a cognitive partner where students critically evaluate AI suggestions (guarding against bias and hallucination). Five guiding questions help educators decide appropriate reliance on AI per discipline and context.

AI enhances personalized and adaptive learning. Machine learning now lets average educators implement adaptive systems that previously required substantial resources, technical expertise, and infrastructure — tailoring content and pacing while providing real-time insights and fostering critical thinking and Problem Solving.

The teacher's role remains essential, and AI enhances rather than replaces it. Educators orchestrate AI-driven experiences for authentic problem-solving, critical thinking, and Collaborative Learning; reduce equity and access gaps; interpret the data AI generates about students; shift from authoritative to collaborative/facilitative teaching; and preserve mentorship, empathy, and context. AI is positioned as a tool, an enabler, and a cognitive partner in the teacher–student relationship.

The Four-Step AI Response Continuum Framework (ignore, address, redesign, redefine), developed by Fowlin from faculty-development workshops, gives educators a staged journey matched to their readiness. "Redefine" treats AI as a subject worth studying — AI literacy, Ethics, and applications — and the paper warns that ignoring AI is dangerous because students may misinterpret its presence as tacit approval for unagreed use.

On academic integrity, the paper cautions against AI detection (unreliable, prone to false positives) and instead advocates addressing root causes of dishonesty — cultivating intrinsic motivation, Self-Efficacy, meaningful coursework, and process-over-product formative assessment.

What this means for practice

• Instructors. Sequence AI introduction with the paper's two-phase approach: build students' core competencies without AI first, then add AI as a cognitive partner and require them to critically evaluate its suggestions for bias and hallucination, which the authors recommend specifically to prevent automation deskilling.
• Instructors. Drop AI detection—unreliable and prone to false positives—and address the root causes of dishonesty instead through intrinsic motivation, Self-Efficacy, meaningful coursework, and process-over-product Formative Assessment.
• Faculty developers. Meet educators where they are with the Four-Step AI Response Continuum (ignore, address, redesign, redefine), which grew out of the authors' faculty-development workshops, and treat silence as risky: ignoring AI can read to students as tacit approval of unagreed use.
• Designers. Unbundle activities à la James Lang into components best done independently and components best coupled with AI, an operational method for Learning Design that keeps educator agency central in the way Finkelstein's principled framework and work on agentic AI both argue technology should support rather than bypass human capacity; the same principle-first scaffold carries over to experiential and situated tasks in health professions and higher education, where the two-phase sequence guards against over-reliance and deskilling by depending on implementation rather than the tool itself, complementing Learning Theories work on how AI can support or undermine deeper cognition.
• Administrators. Fund what makes the principles operational at scale—adaptive systems, personalized pathways, immersive embodied environments, and analytics now reachable by the average educator—and keep educators in the interpretation loop while closing equity and access gaps, the stance teacher-role research supports and studies of critical thinking in medical students locate in the shift from authoritative to facilitative teaching.

Limitations

• This is a theoretical analysis, not an empirical study: it collects no participant or outcome data, so the claim that AI operationalizes the three learning principles at scale is argued rather than demonstrated, and the authors invite future empirical research.
• The framework is developed from one institution's context—health professions education at the Medical University of South Carolina, including the IP 711 course module reaching roughly 900 students across 10 academic programs—so its materials reflect a single site.
• The Four-Step AI Response Continuum is proposed as a readiness-matching heuristic without evidence that educators move through the four stages in that order, or that stage placement predicts successful integration.
• The pedagogical examples (a coastal-ecosystem simulation, a mergers-and-acquisitions negotiation) are illustrations rather than tested interventions, and the paper flags data privacy, security, and the need for teachers to manage AI bias and error as unresolved.

Citation

Fowlin, J., Coleman, D., Ryan, S., Gallo, C., Soares, E., & Hazelton, N. (2026). Empowering Educators: Operationalizing Age-Old Learning Principles Using AI. Education Sciences, 15(3), 393.