A Multi-Complementary Approach to Evaluating Artificial Intelligence-Supported Mind Map Applications in Life Science Education

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Abstract
In this study, a Multi-Complementary Approach (MCA) was employed to assess the effectiveness of Artificial Intelligence-Supported Mind Maps Applications (AISMMA) in a life science course, with the experimental component conducted in a public primary school in Gaziantep, Türkiye. The approach comprised three interconnected stages. In the initial stage, results from the literature were comprehensively analyzed. The analyses revealed that the outcomes of 41 experimental studies produced a strong, positive effect on academic achievement (g = 0.86). In this stage of the research (“post-complementary knowledge”), a pre-test–post-test design was implemented with experimental and control groups comprising 78 primary school students. As a result of this stage, which targeted the identified methodological and contextual gaps, the post-test scores of the AISMMA experimental group were statistically significantly higher (t = 2.85, p < .05, g = 0.68). Finally, in the “complementary knowledge” stage, the outcomes of the first two stages were synthesized, providing a comprehensive examination of AISMMA’s effectiveness in life science instruction. After introducing the MCA and its underlying epistemological rationale, the study’s methodological procedures and findings are presented in a step-by-step manner. A literature review, based on document analysis within the context of “life in our country” in life science curricula, revealed that AISMMA has a positive and significant impact on academic achievement and various supporting variables in life science education. These convergent findings underscore AISMMA’s potential to meet the evolving demands of the digital age, thereby advocating its broader and more systematic integration into life science teaching environments.

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