AI Insight
This pilot study investigated an AI-based teaching program's effect on productive thinking skills among nine female university students at King Faisal University. Using a pre-post test design, researchers measured seven cognitive skills including originality, fluency, flexibility, inference, interpretation, expansion, and imagination. Results showed statistically significant improvements between pre- and post-test scores, though the authors acknowledge the exploratory nature of findings given the small sample and lack of control group.
Why it matters
If validated in larger controlled studies, AI-integrated teaching methods could offer scalable approaches to enhance critical thinking and creative problem-solving skills in higher education. The findings suggest potential applications for educational technology in developing cognitive competencies beyond traditional instruction methods.
Understand the Science
IntroductionThis study aimed to investigate the effectiveness of a teaching program based on artificial intelligence in developing productive thinking skills among female university students.MethodsThe study adopted a quasi-experimental design with a single experimental group. The participants were nine female students enrolled in the Educational Technology course at the College of Sharia, King Faisal University, in the Al-Ahsa Governorate, during the first semester of the 2024–2025 academic year. The sample was purposefully selected from the study population. The instrument used was a Productive Thinking Skills Questionnaire consisting of 20 items distributed across seven core skills: originality, fluency, flexibility, inference, interpretation, expansion, and imagination. To address psychometric robustness, face validity was established via a panel of five expert judges, and construct validity was substantiated by assessing item-total correlations alongside the internal consistency coefficient. A five-point Likert scale was used to score the responses, ranging from “Needs Improvement” to “Excellent.” The reliability of the scale was confirmed with a Cronbach’s alpha coefficient of 0.84. The study applied a teaching program based on artificial intelligence, and data were analyzed using the R statistical software through descriptive and inferential statistics, including means, standard deviations, effect sizes, Cronbach’s alpha, and the Wilcoxon signed-rank test due to the small sample size. Confidence intervals (95% CI) for median differences were computed using exact distribution methods.ResultsThe results revealed statistically significant improvements between pre- and post-test scores, suggesting the potential effectiveness of the program within this exploratory pilot context.DiscussionGiven the small sample size (n = 9), this study is framed as a pilot design; the findings are associative and exploratory, intended to generate preliminary evidence rather than definitive causal inferences. The critical limitations of the single-group design—such as vulnerability to maturation, testing effects, and task familiarity—are extensively evaluated. The study recommends incorporating artificial intelligence-based teaching strategies in university education to enhance students’ productive thinking skills.