Hissah Fahad AlMuhaysh
Introduction This study aimed to investigate the effectiveness of a teaching program based on artificial intelligence in developing productive thinking skills among female university students. Methods The 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. Results The results revealed statistically significant improvements between pre- and post-test scores, suggesting the potential effectiveness of the program within this exploratory pilot context. Discussion Given 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.