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The Mediating Role of Achievement Goal Motivation in the Relationship between Self-Regulated Learning Ability and Academic Achievement in Generative AI-Supported High School Mathematics

Author(s)

Jinchao Lin1,2, Fucai Lang3, Heung Kou1, Mei Li4*

Affiliation(s)

1Sehan University, Yeongam-gun, Jeollanam-do, Republic of Korea 2Zhanjiang Mechanical and Electrical School, Zhanjiang, Guangdong, China 3Zhanjiang Experimental High School, Zhanjiang, Guangdong, China 4Lingnan Normal University, Zhanjiang, Guangdong, China *Corresponding Author

Abstract

This study investigates whether achievement goal motivation helps to explain the relationship between self-regulated learning ability and academic achievement when generative artificial intelligence tools are applied to high school students' mathematics course learning. Examined via descriptive analysis, reliability appraisals, Pearson correlation tests, regression methods, and bootstrap mediation assessments were the responses from 849 Chinese students. The set of three scales showed outstanding internal reliability, having Cronbachs alpha values of 0.904, 0.900, and 0.881.The self-regulated learning ability was linked to both achievement goal motivation and academic achievement. The calculated indirect impact was 0.127 (with a 95% bootstrap confidence interval of [0.091, 0.165]), which made up 21.8% of the total linkage. Consequently, the drive associated with achievement goal motivation accounted for part, but not the whole, of the link between students' self-regulated learning ability and academic achievement. The results suggest that generative artificial intelligence is most advantageous in the field of education when students set goals for its use, assess its reasoning, and improve their own work rather than simply accepting its answers.

Keywords

Generative AI; High School Mathematics; Self-regulated Learning Ability; Achievement Goal Motivation; Academic Achievement

References

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