Artificial intelligence assisted design of a novel cooperative learning technique for higher education
Scientific Reports, cilt.16, sa.1, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 16 Sayı: 1
- Basım Tarihi: 2026
- Doi Numarası: 10.1038/s41598-026-50770-1
- Dergi Adı: Scientific Reports
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, BIOSIS, Chemical Abstracts Core, EMBASE, MEDLINE, Directory of Open Access Journals, Zoological Record, Academic Search Ultimate (EBSCO), Natural Science Collection (ProQuest), Biological Science Database (ProQuest), Biomedical Reference Collection: Corporate Edition (EBSCO), Health Research Premium Collection (ProQuest)
- Anahtar Kelimeler: Artificial intelligence in education, Conceptual mapping, Cooperative learning, Higher education, Pedagogical innovation
- Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
- Hakkari Üniversitesi Adresli: Evet
Özet
Cooperative learning has long been recognized as an effective pedagogical strategy, yet the development of innovative techniques tailored to modern educational demands remains a challenge. This study introduces the Curriculum Concept Constellation Technique (CCCT), a novel cooperative learning technique developed with the support of artificial intelligence (AI). The CCCT employs a metaphorical and visual approach, wherein students collaboratively map key curriculum concepts into visual ‘constellations,’ (Throughout this manuscript, references to ‘constellations’ should be understood as a pedagogical metaphor for conceptual relationships, not as an astronomical or literal representation)—a metaphor representing how individual ideas (like stars) interconnect to form meaningful patterns. This approach fosters deeper conceptual understanding through creativity, role-based collaboration, and peer interaction. Using a mixed-methods explanatory sequential design, the study examined the effects of CCCT on 67 prospective teachers enrolled in a “Curriculum Development in Education” course. Participants were divided into experimental (CCCT) and control (conventional lecture-based) groups. Quantitative results revealed statistically significant improvements in the experimental group’s academic achievement (g = 0.839), co-regulation situations (g = 0.512), and attitudes toward cooperativeness (g = 0.751), with moderate effect sizes. Qualitative feedback highlighted CCCT’s strengths in enhancing communication, teamwork, and engagement, though challenges such as unequal participation and time constraints were noted. The study demonstrates that AI can serve as a valuable generative tool in pedagogical innovation, producing structured cooperative learning techniques based on human-authored prompts. The findings suggest that AI-generated techniques can be effectively implemented in higher education contexts, offering a scalable model for developing tailored instructional methods.