AI Inquiry-based Learning (IBL)

This week I would like to share a recent article which integrates the foundational research-based teaching method of Inquiry-Based Learning (IBL) with Artificial Intelligence (AI). The SoTL paper is entitled, “Influence of AI-driven Inquiry Teaching on Learning Outcomes” by Xie (2023). The following hypotheses were proposed:

H1: The use of AI-driven inquiry teaching can enhance learning outcomes (LO).
H2: The evidence acquisition link of AI-driven inquiry teaching can enhance LOs.
H3: The explanation focusing on the link between AI-driven inquiry teaching can improve LOs.

The author reminds us that IBL aims to cultivate students’ learning abilities in all aspects. AI can assist teachers in organizing effective inquiry activities, formulating scientific explanations, highlighting the relationship between problems and assumptions, and utilizing empirical evidence to solve related problems, thereby enhancing the teaching effectiveness. Through a constructivist lens, the study analyzed the impact of four components of AI-driven inquiry teaching (questioning, evidence acquisition, explanation focus, and evaluation summary) on learning outcomes. Additionally, they investigated the variations in learning outcomes resulting from college students’ familiarity with AI.

Results found that:

The overall Cronbach’s α coefficient and Kaiser–Meyer–Olkin (KMO) value of the questionnaire were 0.863 and 0.865, respectively, indicating good reliability and validity.
The four components—questioning, evidence acquisition, explanation focusing, and evaluation summary—of AI-driven IBL significantly improved LOs at levels of significance of 10%, 5%, 1%, and 10%, respectively.
Students who were relatively familiar with AI tended to acquire higher LOs more easily in AI-driven IBL.

Finally, the author found that the future direction of education will be focused on providing an inclusive and stimulating learning environment where every student can study successfully and their abilities can be nurtured and developed. Therefore, AI-driven IBL can be a valuable instructional approach that aids students in learning through efficient data analysis and algorithmic decision-making. This method also allows for more classroom time to be dedicated to higher-level learning activities, thereby facilitating continuous improvement in LOs.

References

Xie, X. (2023). Influence of AI-driven Inquiry Teaching on Learning Outcomes. International Journal of Emerging Technologies in Learning, 18(23), 59–70. https://doi.org/10.3991/ijet.v18i23.45473