Beyond access and algorithms: a curricular model for inquiry-driven student-AI engagement

Abstract

Purpose This paper proposes a pedagogical framework for cultivating inquiry-driven agency in student engagement with artificial intelligence (AI). It addresses a gap in the AI-in-education literature, in which substantial scholarly attention to material access and algorithmic fairness has been accompanied by comparatively less sustained attention to the learner-side capacities that determine whether AI is engaged with critically or passively. The paper offers three connected contributions: a cyclical model of inquiry-driven learning, a four-phase curricular sequence for student-AI engagement, and an operational assessment counterpart that remains diagnostically meaningful in AI-mediated environments. Design/methodology/approach The paper employs an integrative conceptual approach in the tradition of model-paper scholarship (Jaakkola, 2020). It synthesises five bodies of literature: AI in education and AI literacy frameworks, critical scholarship on educational technology, foundational work on curiosity, dual-process cognition, and adaptive expertise, constructive-developmental theory, and research on question-asking in learning and authentic assessment. Sources were identified through targeted citation tracing supplemented by database searches. Theoretical resources are justified at the point of use rather than methodologically up front. Findings The analysis demonstrates that material access and algorithmic fairness, while necessary, do not on their own account for the learner-side capacities that determine whether AI is engaged with critically or passively. It argues that inquiry-driven agency, the capacity to ask productive questions of AI rather than receive its answers, is a worthwhile object of pedagogical design. The Student-AI Learning Trajectory Model presents this development as a four-phase curricular sequence (AI as Tool, Machine, Object, and Partner), with progression understood as a curricular design decision made by educators rather than a developmental trigger inside the learner. Research limitations/implications The model is conceptual rather than empirically validated, and its applicability to younger learners, neurodiverse learners, and low-literacy contexts has not been fully worked out. The paper signposts several lines of empirical inquiry: longitudinal studies of SALT-informed curricula, methodological development of inquiry-sequence assessment instruments drawing on the Objective Structured Clinical Examination tradition, and comparative studies across institutional contexts and learner populations. The contribution is bounded to the pedagogical dimension and does not address the structural reforms that critical scholarship has identified as necessary for genuine educational equity. Practical implications The paper has implications for curriculum design, educator development, and assessment practice. Curricula can sequence student-AI engagement across phases of increasing sophistication rather than treating AI literacy as content to be covered. Educator development requires sustained investment in pedagogical capacity, particularly the modelling of inquiry-driven engagement in front of students. Institutions should treat the redesign of assessment for the AI era as a pedagogical question rather than an integrity question, with inquiry-sequence assessment offering one operational direction. Equity-oriented investment should prioritise educator capacity over the mass distribution of adaptive learning software. Social implications The cultivation of inquiry capacity in AI-mediated learning environments matters for the kinds of learners that schools and universities send into a world increasingly shaped by automated systems. Without deliberate pedagogical design, AI risks becoming a multiplier of existing educational divides rather than a neutralising force. The model locates responsibility with institutions and educators designing learning environments, not with individual students, and identifies the risk of a pedagogical Matthew Effect in which inquiry-driven pedagogy becomes available primarily to already advantaged students absent institutional investment in educator capacity. Originality/value The paper brings together three conversations that have developed largely in parallel: AI literacy as a set of competencies, evaluative judgement as a response to generative AI's destabilisation of assessment, and curiosity as a driver of self-directed learning. Its distinctive contribution is the articulation of phase progression as a curricular sequence informed by a cyclical model of inquiry learning, complemented by an operational assessment counterpart. The paper offers vocabulary and tools for educators, curriculum designers, and assessment scholars responding to the question of how AI should be integrated into the work of cultivating independent learners.