Artificial Intelligence
3 min read
Generative AI in Education: A Double-Edged Sword for Research-Informed Teaching
In recent years, generative AI tools like ChatGPT have rapidly entered classrooms—not just as teaching aids, but as powerful engines for educational research. A new study by Sowa, Brown, Choi, and Newman (2025) explores how these tools are reshaping teachers’ engagement with research-informed educational practice (RIEP), offering both promising benefits and complex challenges.
The Promise: Making Research Accessible and Actionable
Historically, teachers have struggled to integrate academic research into their practice due to barriers like paywalls, dense academic language, and time constraints. Generative AI tools are changing that. Teachers can now:
- Summarise complex research in plain language.
- Generate lesson ideas based on current evidence.
- Access open-source research through platforms like CORE.
- Create implementation plans tailored to their classroom needs.
Rather than relying on rigid teaching templates, educators are using AI to support ideation—sparking creativity and enabling personalised, research-informed lesson planning. This shift is helping teachers to re-think their existing planning to include new research informed elements and ideas.
The Catch: Reliability and Professional Identity at Risk
Despite these advantages, the study highlights several risks:
- Unreliable outputs: AI tools sometimes produce outdated or fabricated sources, leading to confusion and create uncertainty for teachers in how to best make sense of research evidence and apply this to their practice.
- Technological limitations: Teachers often need to refine prompts or cross-check AI-generated content with traditional sources.
- Professional identity concerns: Some educators feel their role is being blurred or diminished, as AI begins to co-author their teaching plans.
Interestingly, some teachers use AI not to discover new insights, but to validate their existing beliefs—raising questions about whether AI is reinforcing confirmation bias rather than fostering critical engagement.
Reimagining RIEP Theory for the AI Era
The authors argue that traditional RIEP theory—focused on benefits, costs, and signification—needs updating. Generative AI introduces new dynamics:
- Benefits and costs are more fluid: Instant access to research can accelerate ideation, but unreliable outputs can derail it just as quickly.
- Signification is shifting: Teachers may feel empowered or undermined by AI, depending on how it affects their sense of autonomy and expertise.
Practical Takeaways for Schools and Teacher Educators
To harness AI’s potential while mitigating its risks, the study recommends:
- Training teachers to craft effective prompts and critically appraise AI outputs.
- Encouraging collaboration among educators to validate and contextualise AI-generated insights.
- Fostering reflective practice to help teachers maintain a strong professional identity in an AI-enhanced environment.
Conclusion: A Call for Balanced Integration
Generative AI tools are not a panacea—but they are powerful allies when used thoughtfully. As schools and teacher education programs embrace these technologies, the focus must remain on empowering teachers to use research critically, creatively, and ethically.
This study offers a timely roadmap for navigating the evolving intersection of AI and education, urging educators, policymakers, and researchers to rethink how evidence is accessed, applied, and valued in the digital age.
To read the full article see Full article: Examining how generative AI tools benefit and challenge teachers’ research-informed practice