Enterprise AI Analysis
Something Old, Something New: WebQuests and GenAI in Teacher Education
Generative artificial intelligence (GenAI) has emerged as a transformative educational technology, raising questions about how educators and pre-service teachers critically engage with AI-produced content. This case study investigates how WebQuests, a long-established, inquiry-based pedagogical model, can foster critical engagement with GenAI tools. Findings indicate scaffolded engagement encouraged critical evaluation of AI-generated content, fostering digital and AI literacy.
Authored by: Peter Tiernan, Enda Donlon, Mahmoud Hamash, James Lovatt
Executive Impact
This study demonstrates that structured pedagogical approaches, like WebQuests, are highly effective in integrating GenAI into teacher education. By providing a framework for critical evaluation and synthesis of AI-generated and curated content, the study fostered essential AI literacy, preparing pre-service teachers to confidently navigate and ethically apply AI in their future classrooms. The findings highlight the dual nature of GenAI—efficiency vs. trustworthiness—and underscore the need for institutional guidance and professional development.
24
Pre-service teachers in study
82.8%
Voluntary interview participation rate
3
AI Literacy Frameworks Addressed
13
Thematic Analysis Themes Identified
Deep Analysis & Enterprise Applications
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Understanding Digital & AI Literacy
Digital literacy, an umbrella term for competencies in a digital world, includes operational, functional, and ethical aspects (Martin, 2005; JISC, 2015). Information literacy, a critical component, involves locating, evaluating, organizing, and synthesizing data (Churchill, 2020). AI literacy builds on this, adding competencies to understand, use, and critique AI (Long & Magerko, 2020). Key areas include understanding AI concepts, appropriate tool use, critiquing outputs, and recognizing societal/ethical implications (Ng et al., 2021).
Opportunities and Challenges of GenAI
GenAI, with tools like ChatGPT and Copilot, offers personalization, assistive features, and immediate feedback (Lo, 2023; Boulhrir & Hamash, 2025). For teachers, it streamlines assessment, content creation, and can act as a co-teaching assistant (Božić & Poola, 2023; Alghazo et al., 2025). However, concerns include erosion of cognitive skills, academic integrity issues (Memarian & Doleck, 2023; Wild, 2023), data privacy, inherent biases, and potential for misinformation (Angwin et al., 2016; Montenegro-Rueda et al., 2023). Teachers need training and clear guidelines for responsible integration.
The WebQuest Model for AI Engagement
WebQuests are inquiry-based learning activities fostering critical thinking, problem-solving, and information evaluation using web resources (Dodge, 1995). Their structured approach guides students through tasks requiring synthesis and application of information from diverse sources (Bui et al., 2018). In this study, WebQuests scaffolded pre-service teachers' engagement with GenAI, encouraging systematic variation of prompts and comparative evaluation of AI outputs against curated sources, thus building critical evaluation skills (Jovanović & Campbell, 2022).
Developing Evaluative Stance and Future Needs
The WebQuest methodology successfully cultivated a nuanced understanding of GenAI's affordances and limitations, enabling pre-service teachers to develop an evaluative stance rather than uncritical acceptance (Kong et al., 2021). Participants developed strategic source selection, prioritizing curated resources for accuracy while leveraging AI for efficiency. Future recommendations include wider adoption of scaffolded AI pedagogies, clear institutional guidelines, sustained professional development, and longitudinal research on AI's impact on teaching and learning (Tiernan & Donlon, 2024).
WebQuest Pedagogical Flow
The WebQuest structure guided pre-service teachers through a systematic inquiry process, fostering critical thinking and resource evaluation, directly addressing how to engage with complex concepts and tasks.
Introduction
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Task
→
Process
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Resources
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Evaluation
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Conclusion
| Dimension | ChatGPT | Copilot |
|---|---|---|
| Overall Similarity | Minimal difference, quite similar service | Minimal difference, quite similar service |
| Detail and Length | More verbose, waffled more, gave more content | Quicker and more concise |
| Structure and Formatting | Broke questions down, more segmented, well-structured prompt | Generalized information, slightly more direct |
| Style and Tone | Different phrasing, gives a bit more content | Slightly more direct, different presentation |
82.8%
Pre-service teachers volunteered for interviews
This high participation rate underscores the relevance and importance pre-service teachers attach to understanding GenAI in education, reflecting a strong desire for practical engagement.
Scaffolding Critical AI Engagement with WebQuests
Context: A cohort of 24 pre-service language teachers engaged with a WebQuest, comparing AI-generated content (ChatGPT, Copilot) with curated academic resources to create infographics for secondary school students.
Challenge: To equip future teachers with critical evaluation skills for AI-produced content, addressing concerns about accuracy, bias, and trustworthiness in educational settings, and to move beyond surface-level assessment.
Solution: The WebQuest fostered comparative evaluation, requiring participants to systematically vary prompts and assess AI outputs against trusted sources. This built intuition about AI model behavior and critical evaluation capabilities.
Outcome: Pre-service teachers developed a nuanced understanding of GenAI's affordances and limitations, demonstrating strategic source integration and critical thinking—essential for responsible AI use in classrooms and for informing their pedagogical practice.
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Estimated Annual Savings
$0
Annual Hours Reclaimed
0
Your AI Implementation Roadmap
Based on the study's findings and expert recommendations, here's a strategic roadmap for integrating GenAI responsibly and effectively into your organization.
Phase 1: Pilot & Expand Scaffolded Pedagogies
Integrate inquiry-based approaches like WebQuests more widely in initial teacher education to provide practical, hands-on experience with generative AI, fostering critical evaluation beyond functional use. This means designing modules that explicitly require comparison of AI-generated and curated content.
Duration: 1-3 Months (Pilot Phase)
Phase 2: Develop Clear Institutional AI Guidelines
Establish clear, responsible, and transparent policies for GenAI use in educational contexts, ensuring ethical application and academic integrity. These guidelines should address issues of academic honesty, data privacy, and appropriate tool deployment.
Duration: 3-6 Months (Policy Development)
Phase 3: Implement Sustained Professional Development
Provide ongoing training for educators to remain confident and informed about evolving AI technologies. This includes deep dives into AI mechanisms, identification of potential biases, and practical pedagogical integration strategies, ensuring teachers can effectively guide students.
Duration: 6-12 Months (Ongoing Training)
Phase 4: Conduct Longitudinal Research & Impact Assessment
Undertake larger-scale, long-term studies to explore the influence of GenAI tools on teacher learning and practice. Research should examine the transferability of critical evaluation skills developed through structured pedagogies and the ultimate impact on student learning outcomes in school settings.
Duration: 12-24 Months (Continuous Research)
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