Mohamed Hassan Ragab Khalaf

Generative Artificial Intelligence (GAI) has the potential to transform education, particularly in fostering students’ visual thinking skills (VTS) within STEM learning. However, its pedagogical value depends on strategic integration guided by cognitive and instructional principles. This study introduces the 3D4E Strategic Approach—a structured model encompassing Define, Design, Develop, Evaluate, and Enhance phases—to optimize the educational use of GAI visualization tools for improving VTS among STEM high school students. Grounded in Whole Brain Theory (WBT) and Brain-Based Learning Theory (BBLT), the approach operationalizes iterative cycles that engage both hemispheres of the brain to promote conceptual understanding and creative visualization. A quasi-experimental design was employed in a STEM high school with an experimental and a control group. Data was collected using a cognitive VTS test and an observational performance rubric. Results indicated no significant pre-test differences between groups, yet post-test analyses revealed substantial improvements favoring the experimental group on both instruments (cognitive test: M = 37.50 vs. 24.87; p < 0.001, η² = 0.998; rubric: M = 116.00 vs. 81.83; p < 0.001, η² = 0.988). Repeated measures ANOVA confirmed significant time × group interaction effects, and a strong positive correlation ( r = 0.945, p < 0.001) demonstrated convergent validity between conceptual and applied gains. The findings underscore the power of integrating GAI with structured, theory-driven instructional design to enhance higher-order visual thinking in STEM education. The study offers practical implications for curriculum innovation and teacher professional learning and recommends longitudinal and multi-context replications to validate the scalability of the 3D4E model.

Blockchain technology possesses transformative potential across various industries, including higher education, offering improved security, transparency, and efficiency in managing academic records and certification. This study employed an Extended Technology Acceptance Model (ETAM) to examine faculty acceptance of blockchain technology in higher education and inform implementation strategies. A survey instrument based on the ETAM framework was administered to 297 faculty members across some countries in the Middle East. The results indicated generally favorable perceptions of blockchain technology. Regression analyses revealed that both perceived usefulness (β = 0.54, p < .001) and perceived ease of use (β = 0.48, p < .001) significantly predicted faculty intention to adopt the technology. Further analyses identified significant differences in blockchain perceptions based on academic specialty, with science faculty reporting higher perceptions compared to humanities faculty (t(297) = 2.005, p = .046), attitudes toward using blockchain technology (t(297) = 3.161, p = .002), and total blockchain technology in education (BTE) (t(297) = 3.050, p = .002). There were also significant differences observed in the perceptions of faculty members based on their geographical context, showing considerable differences (t(297) = 4.206, p < .001). These findings underscore the importance of implementation strategies designed for academic specialization and geographic context. Perceived usefulness and ease of use remain critical drivers of technology acceptance in higher education, highlighting the need for targeted resources and support to facilitate successful integration. However, the study’s cross-sectional design and exclusive focus on faculty perspectives provide a circumscribed view of implementation complexities and long-term impacts. Future research should incorporate longitudinal data, including student perspectives, and assess technical and organizational readiness. This study also invites further exploration of the impact of targeted training on faculty acceptance and on discipline-specific blockchain use.

Incorporating serious games into educational methodologies has prompted considerable interest from scholars worldwide, especially as a strategy to engage and assist learners with special needs. This study examined the effects of a serious games-based adaptive learning environment (SG-ALE) on communicative abilities and motivation in autistic children within a defined educational context. The SG-ALE, intended to foster active learning and skill enhancement, included various stimulating activities such as puzzles, assembling pictures from shadows, forming words from letters, and creating words from hearing sounds. This environment was meticulously designed to offer individualized learning experiences for children with autism. The study utilized the single-group experimental design, including 14 children from a contemporary private school. The effectiveness of the SG-ALE was evaluated using a newly formulated communication skills rubric (CS-Rubric) and motivation to communicate scale (MC-Scale), both of which demonstrated elevated levels of internal consistency and reliability. The data analysis included paired samples t-tests and analyses of variance to ascertain the statistical significance of observed changes. The results demonstrated statistically significant enhancements in all facets of communication skills (attention and visual communication, motor and verbal imitation, receptive language, expressive language, and attention and auditory discrimination), along with increased motivation to communicate after applying the SG-ALE. The adaptive characteristics of the learning environment demonstrated that it offered children experiences customized to their advancement, underscoring the efficacy of adaptive serious games in promoting more engaging and personalized educational opportunities. This method has demonstrated promise in improving the academic experience of children with autism. The study presents recommendations for designing and implementing serious games in digital learning environments to assist children with special needs, highlighting the necessity for additional research to investigate the various applications of these tools.

The rapid proliferation of artificial intelligence (AI), particularly within education, presents both opportunities and challenges. While AI offers innovative solutions to pedagogical challenges, unstructured utilization of AI tools like ChatGPT can foster passive learning, with students relying on automated solutions rather than engaging in active learning. This quasi-experimental study addressed this concern by introducing and evaluating the R5E pattern, a novel pedagogical model for integrating ChatGPT into programming education to promote active learning and critical thinking. The study compared the efficacy of the R5E pattern with unstructured ChatGPT use in developing programming skills among undergraduate students. Seventy students were randomly assigned to either an R5E group or an unstructured ChatGPT using group. A mixed-methods approach, employing the Programming Skills Cognitive Test (PS-CT) and the Programming Skills Performance Observation Card (PS-POC), assessed both cognitive and performance aspects of programming skills. Pre- and post-intervention data revealed improved programming skills across both groups; however, statistically significant differences favoured the R5E group on both post-intervention assessments. A significant positive correlation between PS-CT and PS-POC scores indicated that increased theoretical knowledge corresponded with enhanced practical performance. Qualitative observations revealed that R5E students engaged in more peer and instructor interaction, alongside ChatGPT use, and posed more sophisticated questions, unlike the second group, which relied more heavily on direct ChatGPT queries. These findings support the superior efficacy of the structured R5E pattern in fostering programming skill development compared to unstructured ChatGPT use. While limitations, including sample size and a short post-test timeframe, necessitate further research, this study suggests the R5E pattern holds promise as a validated pedagogical model for integrating AI into education. Future research should expand the sample, extend the intervention duration, and utilize a delayed post-test.