A Scoping Review of Generative AI Usage and Human Cognition in Education and Professional Practice: Dual-Edged Cognitive Impacts and a Governance Framework
Aug 2026· Davao Research Journal· Vol 17, pp. 45-58· 0 citations
TL;DR
The Cognitive Impact Taxonomy (CIT) provides a structured approach for assessing Gen-AI’s influence on fluid and crystallized intelligence, key cognitive processes, and the trajectory of cognitive impacts leading to severity assessment, and the Integrated Cognitive Symbiosis Framework (ICSF) introduces a three-layer governance model to support responsible and cognitively healthy Gen-AI adoption.
Abstract
The use of Generative Artificial Intelligence (Gen-AI) across education, healthcare, ethics, and professional practice is rapidly increasing. This growth has sparked a polarized debate, with Gen-AI viewed either as a powerful cognitive augmenter or as a potential threat to critical thinking, creativity, and independent reasoning. To examine this issue, the review scope was defined using the PRISMA-ScR guidelines, Joanna Briggs Institute (JBI) methodology, and the Population–Concept–Context (PCC) framework. A systematic literature search was conducted across PubMed, IEEE Xplore, ACM Digital Library, Springer, and Wiley Online Library. Only English-language publications published between 2020 and 2025 were considered. Following multi-stage screening, duplicate removal, eligibility assessment, and human verification, 107 peer-reviewed articles were included in the final analysis. Among the selected studies, 42% focused on university education, 23% on school education, 16% on medical education, 11% on ethics and moral responsibilities, and 8% on professional training. Synthesis of the findings led to two major conceptual contributions. First, the Cognitive Impact Taxonomy (CIT) provides a structured approach for assessing Gen-AI’s influence on fluid and crystallized intelligence, key cognitive processes, and the trajectory of cognitive impacts leading to severity assessment. Second, the Integrated Cognitive Symbiosis Framework (ICSF) introduces a three-layer governance model to support responsible and cognitively healthy Gen-AI adoption. Together, the CIT–ICSF pipeline offers educators, institutional leaders, and policymakers a practical mechanism to maximize the benefits of Gen-AI while mitigating potential long-term cognitive risks.
A conceptual framework offering AI designers, teacher educators, and policymakers a psychologically grounded, empirically mapped, and internationally contextualised basis for developing AI-supported educational systems that advance equity, learner engagement, and meaningful educational transformation is proposed.
Arpana Koul· Review of Artificial Intelli...· 0 citations
The findings show that GenAI supports critical thinking when embedded in structured learning activities that require students to question, evaluate, and revise AI-generated content, and highlight the importance of designing AI-supported learning environments that position AI outputs as objects of reasoning rather than sources of answers.
A context-bounded interpretive framework is proposed suggesting that GenAI’s educational value depends on instructional and governance conditions that preserve autonomy, critical thinking, integrity, and equity.
Mayra Álvarez-Jiménez, Geovanny Cudco, Diego Gamboa et al.· Computers· 0 citations
This systematic review synthesises empirical and conceptual evidence on university pedagogical practices that cultivate critical thinking and, through it, student intellectual autonomy in digitally mediated learning environments and identifies gaps concerning long-term transfer, equity, and the assessment of autonomy in AI-saturated contexts.
Mayra Carolina Márquez López, Daniela Guardo Rua, Hugo Leonardo Gómez Hernández et al.· Journal of Intelligent Decis...· 0 citations
Generative artificial intelligence (GenAI) is rapidly changing professional language education, yet the behavioral and cognitive mechanisms through which learners engage with GenAI-mediated language tasks remain insufficiently synthesized. This critical integrative review reframes translation and interpreting education as a high-cognitive-load case of professional language learning in which students must evaluate AI output, regulate feedback use, and remain accountable for meaning across languages. A documented evidence-selection workflow was applied to an author-curated bibliographic corpus and a supplementary source-expansion corpus comprising 690 potentially relevant records, from which 21 core studies were selected for focused synthesis. The included studies directly addressed GenAI, translation/interpreting education, AI-generated feedback, post-editing, learner revision, interpreter assessment, or AI literacy. The synthesis is theoretically grounded in cognitive load theory, self-regulated learning, feedback literacy, trust in automation, and social-cognitive accounts of agency. Across the reviewed evidence, learners’ engagement with GenAI involves cognitive load redistribution, trust calibration, feedback uptake, metacognitive monitoring, affective responses, and behavioral revision decisions. The review proposes a behavioral model that links technological mediation, cognitive appraisal, self-regulated behavioral engagement, pedagogical regulation, observable process evidence, and professional agency. We argue that the central educational challenge is not whether GenAI improves single-task language performance, but how learners develop calibrated trust, critical judgment, self-regulated feedback use, and professional agency in human–AI language-learning environments. The framework may also inform adjacent AI-mediated language-learning contexts where learners must evaluate feedback, revise language, and justify communicative choices.
This systematic review and meta-analysis examines the impact of generative artificial intelligence (GAI) on cognitive learning outcomes in STEM education. Prior research is growing but remains fragmented, often focusing on usability or single tools like ChatGPT rather than domain-specific cognitive effects. We therefore address these gaps by examining (1) the extent to which interactions with GAI enhance learning effectiveness and possible moderators, (2) what challenges learners face when interacting with GAI systems, and (3) which interventions support successful learner-GAI interaction. We meta-analyzed externally assessed cognitive outcomes (RQ1) and narratively synthesized reported learner challenges and supportive instructional interventions (RQ2-RQ3) when quantitative pooling was not feasible. Two pairs of raters independently screened and coded peer-reviewed quantitative studies published after 2017 that included a comparison/control group and examined cognitive learning in STEM involving learner-GAI interaction. A systematic search (ERIC, PsycINFO, Web of Science; updated May 7, 2026) and citation tracking yielded 85 eligible studies, of which 49 studies with 59 effect sizes met meta-analytic criteria. Most studies focused on higher education and used text-based GAI tools (e.g., ChatGPT). A random-effects meta-analysis shows an overall positive effects of GAI in STEM education, but the studies included exhibit a substantial heterogeneity (
$$I^2=96.32\%$$
), and the prediction interval ranges from Hedge’s
$$g = -1.52$$
to
$$g = 3.20$$
. In line with this, funnel plot asymmetry suggests a potential publication bias. To account for potential publication bias, we also conducted a Robust Bayesian Meta-Analysis (RoBMA) and found that the overall positive effect of GAI in STEM education can be largely attributed to publication bias (
$$\mu =0.076\ \pm \ 0.254$$
), however a large heterogeneity remains (
$$\tau =1.190\ \pm \ 0.166$$
), which appears to be not associated to publication bias. To explain the heterogeneity, we conducted a moderator analysis and found that the learning outcome (knowledge vs. skills) as well the
inversion-substitution-augmentation-redefinition
(ISAR)-level, which compares the cognitive activity of the intervention and control in terms of the
interactive-constructive-active-passive
(ICAP)-level, both explain parts of the observed heterogeneity (
$$R^2=12.5 \%$$
and
$$R^2=13.0 \%$$
, respectively). In contrast to the main effect, the moderator findings were robust under publication-bias correction using RoBMA. Apart from that, a coincidence analysis suggested that no combination of learning outcome type and ISAR level fulfills a sufficient condition for large effect sizes. Additionally, knowledge as a learning outcome type was found to be an almost, but not strictly, necessary condition for large effects when learning with generative AI. Overall, the RoBMA indicates that GAI appears promising in STEM if knowledge gains are targeted as learning outcomes, and when GAI is used to augment students’ learning activities, instead of substituting their activities, where it can be detrimental. Furthermore, numerous studies (
N
=33) reported large effects, but only because the cognitive activities of the students in the intervention and control groups were not comparable. Evidence for RQ2-RQ3 was limited and inconsistently reported; hence, these findings are presented as transparent, caveated qualitative insights rather than generalizable effect estimates. However, substantial unexplained heterogeneity and systematic underreporting of learner-level variables (AI literacy, metacognitive skills) and process-level mechanisms (task delegation, prompt quality, verification behaviors) indicate that the field needs to improve the theoretical models and has yet to measure the factors most likely to drive effectiveness. We propose six testable hypotheses and an integrative theoretical framework to guide future research toward understanding how, for whom, and under what conditions GAI supports STEM learning. The review protocol was preregistered on AsPredicted (ID:176450, URL:
https://aspredicted.org/hpbd-dk75.pdf0
).