AI-Mediated Learning and the Restructuring of Interpretive Cognition: A Developmental-Critical Model for Social Sciences and Humanities Education

Synthesis: Voicu (2026) conceptualizes generative AI not as a neutral tool but as an epistemic mediator that fundamentally restructures interpretive cognition in Social Sciences and Humanities (SSH) education. Using a qualitative, conceptually driven design — systematic interdisciplinary synthesis combined with structured classroom observation of 42 interactional episodes across six sessions with 27 lower-secondary students — the paper identifies three macro-transformations (externalization of interpretive cognition, hybridization of authorship, and emergence of distributed epistemic agency) and maps them onto three developmental trajectories of AI-mediated interpretation (AI-dependent, AI-enhanced, AI-critical). In response it proposes a six-checkpoint developmental-critical pedagogical model Scaffolding critical AI literacy, epistemic responsibility, and interpretive autonomy, arguing that AI integration must preserve rather than erode the dialogic, situated nature of humanistic meaning-making.

Key Findings

Conceptual frame. Moving beyond instrumental views, the paper treats generative AI as an epistemic technology (Floridi) that produces decontextualized yet rhetorically coherent interpretations, simulating understanding without lived, historically grounded horizons. This creates a tension between interpretation as lived understanding and interpretation as computational Simulation, particularly acute in SSH disciplines where meaning-making depends on sustained engagement with ambiguity, context, and dialogic reasoning.

Design. A qualitative, conceptually driven study grounded in a post-phenomenological orientation combined a systematic interdisciplinary synthesis (cognitive science, AI Ethics, digital pedagogy, philosophy of interpretation, developmental psychology) with structured classroom observation at a Romanian lower-secondary school (27 students aged 13–15, six weekly instructional sessions). Analysis was abductive, coding each of the 42 interactional episodes on type of AI involvement, degree of cognitive delegation, and learner epistemic positioning.

Three transformations. (1) Externalization of interpretive cognition — learners delegate thematic identification, contextual explanation, and comparative analysis to AI, increasing efficiency but reducing engagement with ambiguity and productive struggle (risks of shallow learning, cognitive offloading, and interpretive atrophy). (2) Hybridization of authorship — student writing blends human and AI-generated content, often normalizing statistically dominant rhetorical structures, reducing originality and individual voice, and risking uncritical incorporation of fabricated references. (3) Distributed epistemic agency — knowledge production extends across human and algorithmic systems; learners often attribute authority to AI output ("the AI says"), shifting responsibility for evaluation and risking epistemic outsourcing.

Three developmental trajectories. From the 42 episodes, 15 (35.71%) were classified AI-dependent (direct uptake, minimal elaboration or verification), 16 (38.10%) AI-enhanced (selective adaptation, reformulation, contextual integration), and 11 (26.19%) AI-critical (triangulation, questioning, contextual reframing, dialogic use). Fully critical engagement remains the least frequent pattern, indicating a transitional ecology where assisted interpretation dominates but epistemic autonomy is uneven.

Pedagogical model. The developmental-critical model operationalizes progression toward interpretive autonomy through six checkpoints: interpretive grounding, contextual anchoring of AI outputs, critical examination of algorithmic structures, hybrid-authorship transparency, reflexive epistemic positioning, and dialogic engagement under human control. Each checkpoint addresses an observed risk (interpretive shortcutting, epistemic outsourcing, loss of authorial voice) and is grounded in cognitive-developmental theory, hermeneutics, and critical pedagogy.

Implications. Interpretation in SSH should be scaffolded by direct engagement with primary sources before AI use; writing should be reconceptualized as developmental rather than product-oriented; and critical AI literacy (technical, epistemic, ethical) should be integrated across curricula with attention to teacher preparedness. The model advances an analytically generalizable, empirically grounded framework for AI integration that does not compromise interpretive depth.

What this means for practice

• Instructors. Scaffold interpretation with direct engagement with primary sources before any AI use, so learners meet ambiguity and productive struggle instead of delegating thematic identification and contextual explanation.
• Instructors. Reconceptualize writing as developmental rather than product-oriented, and require hybrid-authorship transparency so students disclose and account for AI contributions instead of normalizing statistically dominant rhetorical structures.
• Learners. Treat every AI output as a claim to triangulate: compare it against the source text, question omissions and inaccuracies, and reformulate conclusions in your own words.
• Instructors. Integrate critical AI literacy — technical, epistemic, and ethical — across the curriculum and attend to teacher preparedness, since fully critical engagement was the least frequent of the three observed trajectories.

Limitations

• The empirical component is a single classroom in Iași, Romania — 27 lower-secondary students aged 13–15 in one class across six weekly sessions — so the three trajectories are context-specific and not generalizable.
• Analysis is qualitative and abductive across 42 coded interactional episodes, with no control group and no causal inference; trajectory assignment depended on coder judgment about learner autonomy, verification, and interpretive control.
• The observation window is short (six sessions) with no longitudinal or cross-context follow-up; the authors themselves call for cross-context empirical testing, mixed-method and longitudinal designs, and further work on assessment, curriculum, and teacher training.

Citation

Voicu, C.-G. (2026). AI-Mediated Learning and the Restructuring of Interpretive Cognition: A Developmental-Critical Model for Social Sciences and Humanities Education. Journal of Digital Pedagogy, 5(1), 37–51. https://doi.org/10.61071/JDP.2665