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.

What this means for practice

• Instructors. Score verification as three separate things — whether a student initiated a check, how well the check was done, and whether it resolved the uncertainty — because a strong process can end inconclusive and a weak one can still land on the right answer.
• Instructors. Require the accept, revise, or reject decision to be recorded and justified for each AI suggestion, since an acceptance cannot be judged appropriate without knowing whether the suggestion was correct.
• Designers. Match the intervention to the process it should move — lateral-reading instruction for verification strategy, confidence prompts for metacognitive monitoring, forced delay for reliance decisions, error correction for learning — and test any reduction in acceptance for the rejection of correct assistance it causes, since the objective is proportionate verification rather than maximal skepticism.
• Researchers. Build studies that can make the verification-to-reliance linkage testable: adjudicate AI-output quality, capture verification initiation, code process quality, score verification success, record the accept–revise–reject decision and evaluate it against that adjudication, and follow immediate performance with unaided retention or transfer. Report the AI system, model version, and configuration with every result, since several studies audited in this review left them unstandardized.
• Researchers. Model boundary conditions rather than assuming homogeneous effects: prior and domain knowledge, learner characteristics, task stakes, task verifiability, verification costs, accountability, 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 7 August 2026 search cut-off and rapid model evolution mean the map should be read as a time-bounded snapshot.

• 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.

• Several audited studies did not standardize or report the AI system, model version, or configuration, which limits comparability of reference standards.

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.