Aug 2026· Canadian Journal of Business, Economics and Health Management· 0 citations
TL;DR
This paper synthesizes peer-reviewed and carefully delimited contextual research on how generative AI use relates to undergraduate academic performance, with attention to what Canadian universities can already act on.
Abstract
Generative artificial intelligence (AI) has entered everyday undergraduate study faster than the evidence base has kept up with it, and the evidence that does exist points in opposite directions. This paper synthesizes peer-reviewed and carefully delimited contextual research on how generative AI use relates to undergraduate academic performance, with attention to what Canadian universities can already act on. Searches of Google Scholar, Scopus, Web of Science, ERIC, and ScienceDirect covering 2022 to 2026 produced a two-tier evidence base: Tier 1 peer-reviewed empirical and synthetic studies of student learning outcomes, and Tier 2 supplementary sources admitted under stated justifications, including contextual Canadian and international surveys, one secondary-school field experiment, and one preprint mechanistic study. Experimental syntheses report medium to large short-term gains when ChatGPT is built into instruction. Survey work points the other way: frequent unstructured use tracks with procrastination, self-reported memory problems, and slightly lower grades, and unrestricted access during practice has been shown to depress later unaided performance. Purpose of use reconciles most of that disagreement, because a tool that scaffolds thinking behaves very differently from one that replaces it. Canadian peer-reviewed studies document heavy campus adoption and considerable student ambivalence about integrity and learning, yet almost none link purpose-differentiated use to measured performance. Closing that gap matters for assessment redesign, AI-literacy programming, and the credibility of the credentials Canadian universities issue.
Generative artificial intelligence has become part of the daily academic works for the university students, whether it develops research competence or simply improves the quality and speed of immediate problem solving and response. This integrative literature review examines that question through five related dimensions of undergraduate research competence: information literacy, critical thinking and problem-solving, academic writing, self-regulated learning and research independence, and research ethics. The review synthesizes recent systematic reviews, meta-analyses, empirical studies, and established educational frameworks. However, GenAI looks most useful when it functions as an assistive tool: students use it to possibility generation, feedback receiving, alternatives comparing, or revise their work while retaining responsibility for source evaluation, reasoning, and final decisions. By contrast, substitutive use where the system performs substantial portions of the cognitive or research process creates problems of cognitive offloading, weak source studying, overconfidence, wrong references, and reduced human research independence. The review therefore argues that the central educational issue is not whether the generative artificial intelligence is beneficial or harmful in itself, but under what conditions AI-assisted performance becomes genuine, transferable research competence. Three recurring moderators are especially important: instructional directions, assessment design, and the limit of responsibility respected by the student. The review concludes with a conceptual framework distinguishing assistive from substitutive usage and proposes a research agenda centered on new designs, objective skill assessment, discipline-sensitive studies, and assessments that require students to demonstrate their own reasoning.
Mohamad Rami Al Jundi, Bashar Abdulkareem Alali, Rama Alothman et al.· Research Journal of Maaref U...· 0 citations
A systematic literature review aims to synthesize existing evidence on the evolving roles of mathematics teachers within AI-enhanced educational contexts and to develop a comprehensive framework explaining role transformation, and contributes a holistic conceptualization of teacher role transformation.
Vaijayanti Aphale, Ketki Kher, Vijayanta Bhurale et al.· Journal of Asia Entrepreneur...· 0 citations
Data gathered in a focus group of recent Business Management graduates who took a research methods in their final year is used to identify pointers for how use of generative AI can be incorporated into the teaching of research methods and how this can prepare a forthcoming generation of students for the workplace.
Martin Rich, Amit Rawal· European Conference on Resea...· 0 citations
This article examines the relationship between undergraduate students’ choice of generative artificial intelligence (AI) tools and the cognitive operations mobilized in scientific research, drawing on Vygotsky’s cultural-historical theory and the concept of the Zone of Proximal Development. It is based on an exploratory diagnostic study, with a mixed-methods approach, conducted with 166 undergraduate students at the Federal University of Mato Grosso do Sul, through a questionnaire with closed questions and open fields, treated by descriptive statistics and qualitative reading. The results indicate intensive use of AI — 72.3% of respondents use these tools weekly or daily — yet concentrated in general-purpose generative systems: among those who use AI in any research phase, 95.6% rely exclusively on generative or language-revision systems, with residual presence (2.4%) of AI-layered scientific databases. The open questions reveal concerns about reliability, plagiarism, impoverishment of one’s own learning, and the lack of institutional guidelines. The mismatch between intensity of use and functional adequacy of the tool is discussed as evidence of an unmediated zone of development, in which students build inadequate scaffolds on their own. The article concludes that AI should be understood as a mediating instrument, not as a more capable peer — a role that remains reserved for the teacher, who must mediate the very choice of the tool. An analytical mediation matrix is proposed to guide this teaching practice.
Heloísa Portugal, Carolina Ellwanger· Revista de Estudos Interdisc...· 0 citations
This study examines the use of Artificial Intelligence (AI) in English for Specific Purposes (ESP) learning among English-major students at Trade Union University. Utilizing a mixed-methods approach incorporating both quantitative and qualitative research design, data were collected from 100 third- and fourth-year students through 5-point Likert scale questionnaires and openended questions. Quantitative data were analyzed using descriptive statistics, while qualitative responses were thematically interpreted to support and triangulate the findings. The results indicate that approximately 79% of students use AI frequently, with 94.2% relying on tools such as ChatGPT for summarizing academic materials and understanding specialized terminology. AI was found to significantly reduce cognitive load and support personalized learning. However, notable challenges were identified, including overreliance on AI, concerns about content accuracy, and risks to academic integrity. This study contributes empirically to the growing body of research on AI in ESP by highlighting the dual impact of AI in a specific educational context in Vietnam. It also proposes a conceptual shift toward fostering “critical AI literacy” and suggests pedagogical adjustments in assessment design.
T. Nguyen· International journal of soc...· 0 citations
In academic assessments, artificial intelligence (AI) is currently a revolutionary framework that makes it achievable to evaluate student performance in a variety of academic contexts in an adaptable and structured manner. AI programs have an extraordinary effect on the learning, success and performance of students. With an emphasis on methodology-based, quantifiable outcomes, and usability in actual academic environments, this study provides a comprehensive and thorough analysis of cutting-edge AI approaches such as Machine Learning (ML), Deep Learning (DL) and Natural Language Processing (NLP) published from 2022 to 2026. Additionally, this study evaluates emerging trends like integrated analysis, explainable AI (XAI) and fairness-based models. The primary conclusion of this paper shows that, in complicated circumstances, DL methodologies regularly dominate classical ML techniques in terms of accurate prediction. Moreover, the absence of uniform assessment models, inadequate incorporation of theoretical concepts in education and restricted potential for generalization among databases are among the primary difficulties addressed. This work influences it because it offers a cohesive and thoroughly reviewed benchmark that helps professionals as well as scholars to create accurate, comprehensible and morally sound AI platforms or models. Furthermore, this comprehensive review makes it easier to implement AI evaluation instruments in actual academic institutions.
AI is making software generation faster, but speed does not remove the need for expertise. As more work is delegated to AI, tacit knowledge may become one of the most important human advantages in software engineering. The post Beyond Prompt Engineering: The Role of Tacit Knowledge in Software Engineering appeared first on GPT-Lab.
MIT News · Artificial Intelligence· news.mit.eduSep 16, 2026
