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Awareness of Artificial Intelligence as an Essential Digital Literacy: ChatGPT and Gen-AI in the Classroom

Education

Awareness of Artificial Intelligence as an Essential Digital Literacy: ChatGPT and Gen-AI in the Classroom

S. M. Bender

This discussion explores integrating Generative AI — including large language models like ChatGPT — into subject English classrooms, arguing that students' grasp of Gen-AI must become part of digital literacy. It highlights opportunities to augment reading, viewing and interpretation while addressing plagiarism, equity and access. Research conducted by Stuart Marshall Bender.

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~3 min • Beginner • English
Introduction
The paper situates the emergence of accessible AI content generators (notably ChatGPT) within the surge of public and academic attention from 2022–2023. While forecasts pointed to applications across sectors, education discourse quickly focused on fears of plagiarism and academic integrity. The author argues that English education should move beyond a narrow emphasis on AI’s impact on writing and instead consider Gen-AI’s broader potential and challenges. The article proposes engaging Gen-AI as part of essential digital literacy, emphasizing co-composition practices students will likely encounter in future workplaces. Drawing on workshops with English teachers in 2023, the author aims to reframe English pedagogy to leverage Gen-AI to support reading, viewing, and interpretation, and to assess the quality of students’ prompting as evidence of understanding. The introduction positions the work within ongoing debates that typically transition from resistance to accommodation and equilibrium as educators adapt assessments to differences between human and AI-generated responses.
Literature Review
The paper synthesizes contemporary discourse on Gen-AI in education, noting both optimism and caution. It references discussions on AI’s transformative potential (e.g., applications in medicine, law, education) and concerns about plagiarism and academic integrity. Literature highlights the interactive nature of chat-based AI and its capacity to motivate engagement in reading, writing, and critical thinking (Ciampa, Wolfe, and Bronstein, 2023), while also underscoring ethical and technical issues, including bias inherited from training data (Mehrabi et al., 2021), hallucinations and misinformation risks (Dziri et al., 2022; Ji et al., 2022), and the need for students to understand how AI generates probabilistic text rather than knowledge. The author situates Gen-AI within critical and digital literacy traditions in English (Luke et al., 2018; Pötzsch, 2019), connecting the subject’s social justice commitments to interrogating bias, equity, and access. The review also draws analogies to prior educational technologies (e.g., graphing calculators) to argue for a balanced, supportive integration rather than replacement of core disciplinary practices.
Methodology
This is a conceptual discussion article rather than an empirical study. The author develops arguments through: (1) illustrative dialogues with ChatGPT simulating student prompts and iterative ‘prompt engineering’ for literary/film analysis (e.g., Coleridge’s The Rime of the Ancient Mariner and the film The Shawshank Redemption); (2) practitioner reflections informed by workshops with English teachers in 2023; and (3) alignment with established literacy frameworks (e.g., Freebody and Luke’s Four Resources model) and relevant scholarship on AI, digital literacy, and pedagogy. These demonstrations are used to expose metacognitive processes, diagnose students’ understanding via their prompts, and explore how Gen-AI can scaffold reading, viewing, interpretation, and rhetorical skills.
Key Findings
- Gen-AI should be understood and taught as an essential component of digital literacy in English, extending beyond writing assistance to support reading, viewing, comprehension, interpretation, and critical literacy. - Effective use of Gen-AI requires substantive prior knowledge from students; crafting prompts and iterating (‘prompt engineering’) reveals and depends on their conceptual understanding of texts, genres, themes, and evidence use (e.g., quoting and linking to themes). - AI output is persuasive but fallible; hallucinations, bias, and overconfident tone necessitate explicit instruction in verification, critique, and rhetorical analysis. - A balanced integration model—analogous to the adoption of graphing calculators—positions Gen-AI to support, not replace, core disciplinary skills; learning should be with and about the technology rather than of the technology alone. - Applying Freebody and Luke’s Four Resources, Gen-AI can help students: decode codes/conventions; participate in meaning-making; use texts functionally; and critically analyze/transform texts. - Assessment opportunities include evaluating the quality of students’ prompts, questions, and iterative refinements (including annotations explaining why prompts were chosen) rather than focusing solely on AI outputs. - Classroom activities can leverage Gen-AI to: (a) draft summaries or character/scene analyses for students to verify and contest; (b) examine rhetorical features (voice, diction, active/passive constructions) in AI-generated news-style texts to introduce misinformation literacy. - Equity and access concerns persist (e.g., paywalled premium features); implementation must account for school realities and avoid alienating teachers or widening gaps. - Gen-AI can serve as a More Knowledgeable Other (MKO) within constructivist pedagogy to scaffold drafting, interpretation, and revision, while preserving the value of peer interaction and workshop practices in writing. - Gen-AI is unlikely to replace English teachers; the central value remains students’ ideas, critical/creative thinking, and their capacity to articulate prompts and evaluate outputs.
Discussion
Addressing the central question of how English can productively engage Gen-AI beyond writing, the paper demonstrates that students’ iterative prompting processes enact and reveal critical literacy knowledge. By scaffolding students to interrogate AI outputs, teachers can enhance reading and viewing pedagogy, deepen interpretive practices, and foster metacognitive awareness of genre, evidence, and rhetoric. The balanced-integration approach maintains the subject’s aims (literacy, critical engagement, social justice) while leveraging Gen-AI as a supportive tool. Embedding AI within models like the Four Resources framework aligns technological use with established literacy goals, and assessment of prompts/questions centers student reasoning over AI fluency. Recognizing AI’s biases and hallucinations situates digital ethics and verification as core competencies, preparing students for real-world encounters with AI-authored media. Considering equity and teacher readiness ensures feasibility and mitigates unintended consequences.
Conclusion
Gen-AI competency is rapidly becoming a key digital literacy for students. English education can harness AI as a ‘digital colleague’ to scaffold reading, viewing, interpretation, and rhetorical analysis, not merely to expedite writing. Productive integration emphasizes students’ ideas, clarity of intent, and ability to articulate and evaluate prompts and outputs. Within constructivist traditions, Gen-AI can act as a More Knowledgeable Other, yet should complement—not replace—socially rich workshop practices. The field should avoid both dystopian panic and uncritical evangelism, proceed cautiously with attention to equity/access, and pursue research on classroom impacts and effective combinations of AI with exemplary practices. Ultimately, AI’s value in English rests on cultivating students’ critical and creative capacities to guide, scrutinize, and transform AI-assisted texts—capacities that affirm the ongoing role of English teachers.
Limitations
- The article is a conceptual discussion, not an empirical study; it relies on illustrative AI dialogues and practitioner reflections from workshops, limiting generalizability. - No systematic data collection or formal evaluation of classroom outcomes is presented. - Proposed activities and assessments are exemplars; their effectiveness and scalability across varied contexts remain to be tested. - Equity and access issues (infrastructure, paywalled features) are acknowledged but not resolved. - Rapidly evolving AI capabilities may outpace specific examples or recommendations.
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