Synthesis: Same AI, different pathways: Unpacking mechanisms of AI-mediated learning across discipline-institution contexts
Key Findings
A mixed-methods study of undergraduates across two discipline–institution contexts in Thailand — Design students at Raffles International College Bangkok (n = 221) and Business (BBA) students at Siam University (n = 222), recruited from Years 1–3 — modeled how AI literacy translates into coursework performance through two behavioral routes and two mediating mechanisms.AI literacy significantly predicted both prompting proficiency and verification behavior (p < .001), confirming the proximal-behavior hypotheses; prompting proficiency was in turn positively associated with AI-supported calibration accuracy, while verification behavior was negatively associated with perceived task-related cognitive load.Bootstrapped indirect effects of AI literacy on assignment quality were supported in both cohorts (bias-corrected 95% CIs, 5000 resamples): the prompting route (e.g., Design estimate .072, p < .001; Business .051, p = .001) and the verification→lower-load route (e.g., Design .034, p < .001; Business .024, p = .001).The most performance-proximal route differed by discipline: verification behavior directly predicted task quality in the Design cohort, whereas AI-supported calibration accuracy — plus a direct AI-literacy path (p < .01) — predicted subject-specific assignment quality in the Business cohort, consistent with each discipline's "task ecology" (open-ended ideation vs. audit-like analytic standards).Measurement invariance across the two cohorts was supported (ΔCFI ≤ .010), and the multigroup models showed acceptable fit (χ2/df below 3; CFI/TLI/IFI above .90; residual-based indices in acceptable ranges).In 20 semi-structured interviews (thematic saturation reached by the 18th), Design students described using ChatGPT mainly to accelerate ideation and refinement, while Business students used it for structured analysis and to make their work more auditable; across both contexts, verification functioned as a metacognitive safeguard that reduced overload and stabilized accuracy.Study Design & Method
The quantitative strand surveyed students immediately after course-embedded assignments, measuring AI literacy, prompting proficiency, verification behavior, AI-supported calibration accuracy (CAL), and perceived task-related cognitive load (ECL) via self-report, with rubric-scored performance outcomes (task quality for Design; subject-specific assignment quality for Business). Multigroup structural equation modeling with measurement invariance testing and bootstrapped indirect effects was used; for example, the configural invariance model reported χ2(170) = 356.1, CFI = .958, TLI = .942, RMSEA = .059, SRMR = .048. The qualitative strand consisted of 20 semi-structured interviews with a purposive, maximum-variation subsample, analyzed thematically in NVivo until saturation at the 18th interview; integration used joint displays mapping SEM paths to interview themes, with an audit trail and explicit attention to discrepant cases.
Implications for AI in Education
The findings argue for operationalizing AI Literacy in coursework as a set of teachable, assessable practices — prompting with intent, verifying with method, and relying with calibrated trust — rather than general tool familiarity. Practical levers suggested by the author include rubric-anchored Prompt Engineering instruction, short "prompt studios" (10–15 minutes) embedded in tutorials, low-friction verification checklists and "evidence traces" (a short appendix of links/DOIs plus a 2–3 sentence rationale) for high-stakes assignments, and assessment policies differentiated by task ecology that reward documented judgment rather than volume of AI use — for example, reflective prompts asking students where they relied on, rejected, or revised AI output. The results also connect AI-mediated learning to Metacognition and to the Cognitive Offloading risks of AI-assisted work, suggesting verification routines as a counterweight.
Limitations
The design is non-experimental, so causal claims remain tentative despite theory-consistent directional modeling. Mediators were assessed via post-task self-report, and residual common-method bias cannot be fully ruled out. CAL captured students' self-assessed assignment quality and rubric fit rather than system-level trust calibration, and ECL may blend extraneous load with intrinsic task difficulty and time pressure. The single-author mixed-methods design creates structural risks that procedural sequencing mitigates but does not eliminate, and the sample draws on two programs within a single national context, bounding generalizability to local curriculum expectations, institutional policies, and discipline-specific assessment norms.
Connected Concepts
AI LiteracyGenerative AIHigher EdPrompt EngineeringConnected Articles
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Shen, Q. (2026). Same AI, different pathways: Unpacking mechanisms of AI-mediated learning across discipline-institution contexts.