In an unscaffolded GenAI-permitted instructional design course, the control group outperformed the experimental group on posttest instructional design performance¶

Claim · All claims
Evidence · 1 study · causal r2 · q3 · i3 large
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q3 i3 After controlling for pretest scores, the control group's adjusted posttest instructional design scores were significantly higher than the experimental group's, with a large effect size. → Zhang Jun 2026

Evidence¶

Zhang Jun 2026¶

Zhang Jun, Peng Yuting, Deng Xinyue, Zeng Qin, Wang Kai. (2026). The Impact of Unscaffolded GenAI Use on Pre-Service Teachers’ AI Readiness, Self-Regulated Learning, Critical Thinking, and Instructional Design Performance: A Quasi-Experimental Study. Behavioral Sciences. https://doi.org/10.3390/bs16071114

q3 · i3 · causal · r2

One-way ANCOVA on posttest instructional design performance with pretest as covariate, comparing the GenAI-permitted experimental group (adjusted mean 81.87) with the control group (adjusted mean 86.54) in a quasi-experiment with 52 pre-service chemistry teachers. The article reports "F = 8.348, p = 0.006, η2 = 0.146", interpreted as large.

"The ANCOVA results indicated a significant between-group difference in posttest instructional design performance after controlling for pretest scores, F = 8.348, p = 0.006, η2 = 0.146."

Discussion¶

Related Claims¶

• Both the GenAI-permitted and control groups improved significantly in instructional design performance and AI readiness over the course, but not in SRL or critical thinking — related
• Unscaffolded GenAI access did not significantly change pre-service teachers' self-regulated learning relative to traditional learning — related
• Unscaffolded GenAI access produced no significant advantage in pre-service teachers' AI readiness compared with traditional learning — related
• Unscaffolded GenAI access was not associated with a significant difference in pre-service teachers' critical thinking relative to traditional learning — related
• AI-supported PBL students obtain significantly higher posttest computational thinking scores than traditional-lecture students when controlling pretest scores — related