Teaching with Generative Artificial Intelligence: Enhancing Critical Thinking and Ethical Awareness in Academic Writing

Synthesis: Benali Taouis & Díaz García (2026), Journal of University Teaching and Learning Practice 23(5). This study integrates generative AI tools — ChatGPT (GPT-4) and Writefull for Word — into the English for Professional and Academic Communication (EPAC) course at the Universidad Politécnica de Madrid (UPM), an academic writing course. Framed by critical digital pedagogy (CDP), self-regulated learning (SRL), and Bloom's revised taxonomy, the intervention guided 96 final-year Spanish undergraduate students (in 24 project groups) through a sequential drafting–revision–reflection workflow while developing research-proposal (RP) sections. Analysis of 24 reflective reports (inductive thematic analysis with grounded-theory coding) showed that the process of verifying, revising, and adapting AI-generated content strengthened students' critical thinking and assessment skills, deepened content awareness, and fostered ethical, responsible academic practice. Rather than banning AI, the study models guided, intentional integration in which AI functions as a cognitive and linguistic scaffold — not a substitute for original thinking.

Key Findings

Verifying AI content strengthens critical thinking and assessment skills. Confronting ChatGPT's hallucinations forced students to validate sources and fact-check outputs, turning initial drafts into objects of critical scrutiny rather than finished text. Students reported that the "critical thinking component lies in the human interpretation" of AI output, positioning themselves as active editors and evaluators.

Students were active agents, not passive consumers of AI. Across group reports, learners demonstrated metacognitive engagement and critical reflection: refining prompts, rephrasing vague outputs, rejecting unhelpful suggestions, and interrogating both ChatGPT's claims and Writefull's stylistic recommendations (e.g., discarding "impact"→"effect" substitutions that shifted meaning).

Two complementary tool roles emerged from thematic analysis. ChatGPT functioned as a generative and reflective writing companion (idea generation, gap identification, literature-review support, contextualised prompting), while Writefull functioned as a linguistic coach for grammar, vocabulary, and academic tone. Together they address higher-order (idea development, reflection) and lower-order (accuracy, style) aspects of writing.

SRL and Bloom's taxonomy structure the cognitive progression. Activities were deliberately aligned with Bloom's revised taxonomy — from remembering (recalling RP structure, grammar rules) through applying (prompt design, draft generation), analysing (checking for hallucinations, bias), and evaluating (judging source credibility) to creating (synthesising AI input with original ideas). Students were required to justify every acceptance or rejection of AI feedback, reinforcing autonomy and responsibility.

AI reduces linguistic anxiety via dynamic scaffolding. Writefull alleviated language-related insecurity in this L2 (English) context by offering immediate grammatical and stylistic corrections, boosting student confidence while preserving authorship — provided students maintained a critical lens on its occasionally inconsistent suggestions (e.g., British vs. American English).

AI is a support, not a substitute, for critical writing. By limiting AI use to just two RP sections and mandating documentation (screenshots, justifications, analytical reports), the design prevented over-reliance and transferred learning from instruction to practice, supporting reflective, self-regulated, and ethically aware academic writers.

Study Design & Method

Context & participants. Conducted within the EPAC course for computing-related degrees at UPM (Escuela Técnica Superior de Ingenieros Informáticos), 2024–2025 academic year. Ninety-six final-year Spanish undergraduates self-organised into 24 interdisciplinary groups of four across four degree programmes (Computer Engineering; Mathematics and Computer Science; Data Science and AI; Computer Engineering & Business Administration double degree). Ethical approval obtained; voluntary, anonymous, minimal-risk participation with informed consent.

Theoretical framework. The design integrated three complementary lenses: critical digital pedagogy (ethical, reflective, socially aware engagement with technology, rooted in Freire's critical consciousness) to guide critical evaluation of AI content; self-regulated learning (goal-setting, monitoring, reflective evaluation per Zimmerman and Pintrich) to build autonomy and metacognition; and Bloom's revised taxonomy to scaffold cognitive progression from lower-order to higher-order thinking.

Teaching intervention. After a training session on responsible AI use (bias, hallucinations, citation accuracy, prompt engineering), students drafted the Introduction and Innovation sections of a research proposal using ChatGPT (GPT-4), writing precise, content-aware prompts and screenshotting the process. In the revision phase they refined drafts with Writefull for Word, documenting modifications and justifications. A reflection phase required justifying acceptance/rejection of edits and verifying ChatGPT-generated citations. Final versions were submitted via Moodle as part of formative assessment.

Measures & data collection. Multiple sources: AI-generated drafts, student revisions, interaction screenshots, analytical reflection reports (24 two-page reports guided by an assessment rubric), and final RP submissions.

Data analysis. Inductive thematic analysis (Braun & Clarke) informed by grounded-theory coding (Corbin & Strauss), with open coding → axial coding → two overarching themes. Intercoder reliability was assessed on a random 20% sample; ChatGPT was used solely as an analytic cross-check, with final coding decisions retained by researchers. Researcher reflexivity and transparent documentation supported trustworthiness.

Implications for AI in Education

This study offers a transferable model for higher education courses seeking to harness generative AI productively rather than prohibit it. Its central lesson — that the educational value of AI lies not in the technology itself but in the pedagogical intentionality with which it is embedded in cognitive, metacognitive, and ethical frameworks — generalises across disciplines and institutions. By aligning AI tasks with Bloom's taxonomy and self-regulated learning, educators can convert AI tools into scaffolds for higher-order thinking and learner autonomy, while co-constructed classroom norms and reflective assignments uphold academic integrity and ethical literacy. The study's emphasis on documenting and justifying AI use (via screenshots and analytical reports) speaks directly to authentic, process-visible Assessment design in AI-rich classrooms. For institutions, it reinforces the need to support AI literacy, prompt engineering skill, and curriculum redesign so that both educators and students engage with AI critically and responsibly.

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Citation

Benali Taouis, H., & Díaz García, A. (2026). Teaching with generative artificial intelligence: Enhancing critical thinking and ethical awareness in academic writing. Journal of University Teaching and Learning Practice, 23(5). https://doi.org/10.53761/28y4hw95