Beyond checking: verification quality, reliance calibration, and learning in generative AI-assisted higher education

Synthesis: This Mini Review argues that “critical” or “responsible” AI use is an over-broad label that collapses seven analytically separable measurement targets: epistemic evaluation, verification initiation, process quality, verification success, reliance decisions, immediate task performance, and independent learning. It defines reliance calibration as a judgment about whether a reliance decision was appropriate given the actual quality of the AI output — an output-contingent classification, not a stage on a timeline, and not the same thing as Trust. Across 493 deduplicated records and 14 priority empirical studies, no study measured verification success and the following reliance decision together against an independently adjudicated standard of output quality, and few looked past immediate performance to delayed retention or transfer.

Key Findings

• Checking is not the same as successful verification. Recording that a learner attempted a check says nothing about how well the checking was done or whether it resolved the uncertainty. The review keeps initiation, process quality, and success apart, because a strong process can still end inconclusive and a weak one can occasionally land on the right answer — initiation is not success.
• Trust and reliance can diverge. Amaro et al. (2024) found that false ChatGPT information changed participants’ reported trust, but the study never measured whether they later accepted or rejected a specific recommendation. A trust scale, on this account, cannot stand in for an observed reliance decision.
• Reliance decisions cannot be classified without ground truth. Chen and Lou (2026) logged accept/reject decisions on ChatGPT Feedback among 78 translation students, but without independent expert evaluation of each suggestion an acceptance cannot be called appropriate, nor a rejection justified. Reliance scales and credibility–trust models reviewed here describe patterns of use without conditioning on whether the AI output was actually correct.
• The central gap is the link from verification success to reliance calibration. Among the 14 priority empirical studies, none measured whether verification succeeded and then classified the subsequent reliance decision against an independently adjudicated output-quality standard; delayed retention or transfer was also uncommon.
• Designs with a reference standard exist, but only next door. Dávila et al. (2025) used advice that was correct about half the time and found that how much students weighted it varied with prior knowledge and gender. Zheng et al. (2025) separated appropriate from inappropriate reliance in a field study of student–ChatGPT quiz conversations and identified a “failed application” pattern, in which following correct guidance still ended in a wrong answer.
• AI literacy does not uniformly increase checking. Rheu and Cho (2025) reported a dimension-specific pattern: understanding Large Language Models (LLMs) processes was associated with more self-reported fact-checking, whereas some forms of Self-Efficacy and feature knowledge were associated with stronger machine heuristics and less checking.
• Correction becomes a learning opportunity only when it demands substantive work. In an error-correction paradigm, Olney and Cade (2025) found that effort during correction mattered for learning; simple answer substitution is unlikely to deliver the same benefit.

Study Design & Method

This Mini Review (Frontiers in Psychology, published 11 September 2026) pairs a targeted, non-systematic evidence synthesis with a conceptual measurement framework. The authors ran complementary topic searches in the Web of Science Core Collection (2022–2026, all editions), updated through 7 August 2026; after DOI, accession-number, and title deduplication the searches yielded 493 unique records. Two authors independently screened the corpus and reconciled their judgments, and priority was given to studies that operationalized a focal target, linked adjacent targets, or exposed a measurement boundary. The evidence set deliberately mixes small qualitative studies, direct higher-education GenAI studies, adjacent AI-advice and HCI designs, and foundational or mechanistic sources.

A focal table maps ten representative studies (Urban 2025; Choi 2025; Chen and Lou 2026; Zhang 2025; Dávila 2025; Zainuddin 2026; Hou 2025; Pudasaini 2026; Zheng 2025; Hu 2026) onto verification, reliance, and task- or learning-outcome columns, with a diagnostic implication for each. Supplementary File 1 carries the search strategies, screening and appraisal detail, operational definitions, calibration metrics, guidance on mixed-effects analysis, and reporting standards.

Implications

• Evaluate interventions for the specific process they target. A lateral-reading lesson should be assessed for its effect on verification strategy and, separately, on verification success; a confidence prompt may mainly shift metacognitive monitoring; a forced-delay interface may change reliance decisions; and an explanation or error-correction activity affects learning only if it induces substantive processing. A single self-report measure of “responsible use” would blur these distinct mechanisms.
• Design studies that can make the linkage testable. Specify or adjudicate AI-output quality, capture verification initiation, code process quality, score verification success, record the accept–revise–reject decision, evaluate that decision against AI quality, and — where learning is intended — follow immediate performance with unaided retention or transfer.
• Watch for the cost of over-correction. Interventions that reduce inappropriate acceptance must also be tested for unintended rejection of correct assistance; the educational objective is effective, proportionate verification that supports calibrated reliance while preserving the cognitive work learning requires.
• Model boundary conditions rather than assuming homogeneous effects. Prior and domain knowledge, learner characteristics, task stakes, task verifiability, verification costs, accountability, AI system and configuration, and multidimensional AI literacy are analytic expectations, not established moderator effects.

Limitations

• Searches were targeted rather than systematic, so the review estimates neither prevalence, effect sizes, nor causal sequence.
• The evidence set mixes small qualitative studies, direct higher-education GenAI studies, adjacent AI-advice and HCI designs, and foundational or mechanistic sources, which limits the strength of inference.
• The seven-target map is an analytic ordering, not a validated causal model — and explicitly not a fixed temporal sequence, since learners may accept and then verify, or revisit evaluation after post-hoc doubt.
• The 7 August 2026 search cut-off and rapid model evolution mean the map should be read as a time-bounded snapshot.
• Several audited studies did not standardize or report the AI system, model version, or configuration, which limits comparability of reference standards.

Connected Concepts

• Trust Calibration — the paper’s core definition: reliance is calibrated only when a reliance decision is judged against an independently adjudicated reference standard for AI-output quality.
• Metacognition — the review separates metacognitive monitoring (epistemic evaluation) from regulatory action (verification) and from learning.
• Cognitive Offloading — externalized cognitive work is offered as the mechanism by which a defensible reliance decision can still yield little learning.
• AI Literacy — treated as multidimensional and shown to influence checking non-uniformly rather than uniformly increasing scrutiny.
• Self-Regulated Learning — independent learning spans retention, transfer, unaided performance, and independent error detection after AI support is withdrawn.
• Critical Thinking — “critical AI use” is the broad label the paper decomposes into measurable targets.
• Higher Education — the population and setting of the review’s evidence base and its boundary conditions.

Connected Articles

• Trust and Reliance on AI in Education: AI Literacy and Need for Cognition as Moderators — directly treats the trust-vs-reliance distinction that this review centralizes.
• Why we believe chatbots: trust calibration as a design problem — design-side account of calibrating trust in chatbot interactions.
• From Plausibility to Verifiability: The PEARLS Framework for Developing Epistemic Agency in Generative AI-Mediated Higher Education — same epistemic-verification construct, different operationalization.
• Agreement Is Not Quality: Blind Expert Verification of Human and LLM Qualitative Coding When Human Consensus Is Not Ground Truth — empirical case of agreement with an LLM being mistaken for verification quality.
• Modeling AI Overreliance as a Complex Adaptive System — overreliance mechanisms complementing this review’s boundary conditions.
• Measuring How Students Rely on Generative AI in Academic Writing: Development and Multi-Source Validation of the Generative AI Reliance Types Scale (GenAI-RTS) — reliance-pattern measurement that this review cites as unable to classify calibration without reference-standard conditioning.
• Metacognitively Discordant Completion and the Aware Pass-Through of Non-Understanding in Generative AI Learning — metacognitive monitoring vs completion behavior in GenAI tasks.
• Is Solving Better Than Evaluating GenAI Solutions? — distinguishes task success from the evaluative work that supports learning.

Citation

Wei, J., & Shang, Y. (2026). Beyond checking: verification quality, reliance calibration, and learning in generative AI-assisted higher education. Frontiers in Psychology, 17, 1965371.