Parisa Applegarth
Higher education institutions have adopted artificial intelligence tools faster than they have developed the pedagogical reasoning needed to place those tools inside a coherent theory of learning. The result is a body of work that documents applications one at a time, chatbots that scaffold reflection, virtual clinics that simulate professional practice, generative tools that personalize explanation, without an account of how these applications relate to the structure of experiential learning or to the competencies that experiential learning is meant to produce. This paper develops the AIMEL framework (Artificial Intelligence Mediated Experiential Learning) in response. AIMEL integrates four bodies of theory that have so far been treated separately: Kolb's experiential learning cycle and its later revisions, research on artificial intelligence in higher education, the sustainability competence literature, and the entrepreneurial competence literature. The framework has three layers. A mediation layer specifies four functions through which artificial intelligence can participate at each stage of the experiential cycle. A competence layer connects those stages to specific sustainability and entrepreneurial competencies rather than to generic learning gains. A governance layer sets four boundary conditions, transparency, fairness, privacy, and human agency, that determine whether a given instance of mediation is legitimate. The paper states seven propositions, offers candidate operationalization for each construct, and sets out implications for curriculum design, faculty development, and institutional policy. AIMEL treats artificial intelligence as a conditional participant in the teaching transaction whose contribution must be earned at each stage rather than assumed.