Aug 2026· Communications of the ACM· Vol 69, pp. 48-56· 0 citations· 3 references
TL;DR
A three-week midterm project embedded in an undergraduate “AI-for-all” course investigated whether AI literacy can be taught to undergrads, and shows any user how to test an AI system rather than trust it blindly.
Abstract
There is ongoing academic debate on whether one can teach AI literacy to undergraduate students across majors, and if yes, how. This article reports a case study: a three-week midterm project embedded in an undergraduate “AI-for-all” course. Students designed reasoning tasks, ran controlled comparisons across widely used chatbots, and evaluated both answer correctness and explanation validity. Through field experience, students with no STEM background learned what consumer chatbots can and cannot do, documenting systematic brittleness across models that “sounded right but reasoned wrong.” More critically, students built understanding of how to evaluate AI outputs. The midterm gave them agency as investigators rather than passive users. Eager to share their discoveries, they are co-authors of this article. Together, we offer here to educators and the broader scientific community a concrete example of the operationalization of AI literacy as experimental practice. The method, however, is not specific to the classroom. It shows any user how to test an AI system rather than trust it blindly. In three-week midterm project, students investigated whether AI literacy can be taught to undergrads.
A narrative and strategic framework for student engagement that emphasises enjoyment, active participation, and personal development is developed by fostering environments where students experience intrinsic motivation and recognise the value of their own skills development.
John F. Grant· Journal of Scholarship of Te...· 0 citations
It is proposed that a quantifiable metric, the Conceptual Difficulty Gap (CDG), may be useful for identifying a class of texts that syntactically appear to be simple, but consistently trigger performance failures.
Igor Crk, E. Gultepe· Education sciences· 0 citations
Evidence is found for two mechanisms behind the learning gains: students shift time away from drafting text and toward reading and searching for information, and they report greater learning enjoyment.
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
The paper proposes a set of guiding principles derived from the identified tensions, emphasising teacher-mediated interaction, developmental calibration of AI use, transparency, curriculum alignment, privacy protection and equity considerations, which provide a structured basis for integrating AI in ways that support learning processes while mitigating potential risks.
The idea of AI-resilient assignments maintaining academic integrity with product- and process-based evaluation approach is proposed, similar to the open-book system, where they require human intelligence, independent thinking, and personal understanding to solve them correctly.
A. Sahu, Chandrakant Kumar Singh, A. K. Malik· International Journal of Sci...· 0 citations
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