From gatekeeper to architect: operationalizing AI as a cognitive partner in higher education

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• Semantic Scholar2026-07-21 07:15:19
  From gatekeeper to architect: operationalizing AI as a cognitive partner in higher education

  In higher education, debates about Generative Artificial Intelligence (GenAI) often polarize around academic integrity risks and efficiency gains. A growing body of post-2023 work has begun to move beyond this dichotomy, proposing constrained tutoring systems, Socratic dialogue agents, and adaptive pedagogical scaffolds built on large language models (LLMs). However, these efforts typically target a single instructional function (e.g., Socratic questioning, problem-by-problem scaffolding, or guardrailed answer generation) and treat the cognitive demands of learning as undifferentiated. An instructional architecture that explicitly aligns distinct LLM configurations with the qualitatively different cognitive operations required across learning phases is not available in the literature yet. We address this gap by presenting a simulator-based framework that decomposes instruction into three functionally distinct, sequentially gated simulators: (i) structured comprehension with explicit depth regulation, (ii) schema-based application and analysis under progressively increasing demands, and (iii) evaluation and creation under instructor-defined epistemic uncertainty. Each simulator is grounded in a specific cognitive theory (Zone of Proximal Development, dual-process accounts of cognition, and epistemic cognition, respectively) and is operationalized through explicit constraints, transition criteria, and non-normative diagnostic rubrics. The framework conceptualizes LLMs as constrained instructional simulators whose pedagogical value derives from how they are configured, bounded, and sequenced by the instructor. The primary contribution is therefore not a new tutoring paradigm but a theory-aligned architecture for sequencing multiple, functionally distinct LLM configurations within a single instructional design. Rather than asserting a solution to Bloom's 2-Sigma Problem, the framework demonstrates how LLMs can scale the specific instructional mechanisms associated with individualized tutoring while preserving disciplinary standards and pedagogical authority. The framework is conceptual and design-oriented; empirical validation is identified as a necessary next step.