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Post 2

harmonypierce
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Cooperative and Inquiry-Based Learning and AI Misconceptions

Misconceptions about artificial intelligence (AI) are common occurrences, despite AI use becoming more popular in educational and workplace settings. With the many misconceptions about AI, it can be tricky to decide where to begin and how to get accurate information out there. Cooperative and inquiry-based learning strategies can help learners to questions these misconceptions and work together.

Cooperative learning is an evidence-based strategy where teachers guide student interactions by having learners work together in small groups with shared goals and individual accountability, much like the Blueprint assignment. Abramczyk and Jurkowski (2020) highlight how cooperative learning differs from traditional group work, with its primary focus on positive interdependence and structured interaction. When implemented effectively, cooperative learning supports both academic and social outcomes. This approach is especially relevant for addressing AI misconceptions, as learners get to hear alternative perspectives and collaborate through misunderstandings.

Inquiry-based learning is focused on answering questions through investigation. Zheng et al. (2026) defines inquiry-based learning as an exploratory process where learners can generate questions, form hypotheses, conduct investigations, analyze data, and reflect on their findings. Their empirical study showed that inquiry-based learning supported by generative AI tools significantly improved learners scientific inquiry skills, knowledge of the information, and overall performance compared to traditional inquiry approaches. They also note that AI use is most effective when it guides learners through an inquiry, rather than providing direct answers. Inquiry-based learning targets many areas of addressing misconceptions because it encourages learners to explore how AI systems work, test claims, and reflect on the limitations of this technology. Zheng et al. (2026) highlights that AI tools can be informative, however, they also carry risks, such as over reliance or providing wrong/misleading information.

Together, both cooperative learning and inquiry-based learning provide good strategies for addressing AI misconceptions. Cooperative learning supports structured social interactions and shared understandings, while inquiry-based learning promotes investigation and reflection.

Photo by Getty Images on Unsplash

References

Abramczyk, A., & Jurkowski, S. (2020). Cooperative learning as an evidence-based teaching strategy: What teachers know, believe, and how they use it. Journal of Education for Teaching, 46(3), 296–308. https://doi.org/10.1080/02607476.2020.1733402

Zheng, L., Liu, Z., Fu, Z., & Liu, S. (2026). An empirical study on leveraging generative artificial intelligence in promoting inquiry-based learning. Journal of Computers in Education. https://doi.org/10.1007/s40692-025-00383-w

One Comment

  1. I really enjoyed reading your post about using cooperative and inquiry based learning to help students understand AI myths and misconceptions. I liked how you explained cooperative learning and how working together allows students to hear different perspectives. That makes a lot of sense when it comes to challenging misunderstandings about AI.

    I also thought your connection to inquiry based learning was strong. Giving students the chance to ask their own questions and explore how AI actually works seems like a great way to build critical thinking. It feels more meaningful than just telling students what is true or false. Exploring real examples of AI tools would probably make the learning more engaging too.

    Thanks for sharing your ideas. Your post clearly showed how these approaches can support deeper understanding.

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