An Experiential Design Learning Model Within a Digital Learning Ecosystem for Enhancing AI Competencies and Instructional Innovation in Pre-Service Science Teacher Education
Bibliographic Data
| ID | 22049201 |
|---|---|
| Authors | Somsak Techakosit (0000-0001-8624-6535, Kasetsart University), Teerapop Rukngam (0009-0002-6312-840X, Kasetsart University, corresponding author), Jarumon Nookhong (0000-0002-3371-3205, Suan Sunandha Rajabhat University), Panita Wannapiroon (0000-0001-8633-5781, King Mongkut's University of Technology North Bangkok), Iot Iot (King Mongkut's University of Technology North Bangkok) |
| Year | 2026 |
| Volume | 16 |
| Issue | 2 |
| Pages | 314 |
| Publication date | 2026-02-14 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Education Sciences (JOURNAL) |
| Journal identifiers | ISSN: 2227-7102 • E-ISSN: 2227-7102 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/educsci16020314 |
| OpenAlex | W7128903429 |
| Language | EN |
| References cited | 21 |
The increasing integration of artificial intelligence (AI) in education highlights the need for teacher preparation programs to support pre-service teachers in developing pedagogically grounded and ethically responsible AI competencies. This study designed and preliminarily examined an Experiential Design Learning model within a Digital Learning Ecosystem (EDL–DLE) to support the development of AI competencies and instructional innovation in pre-service science teacher education. A four-phase research and development framework was employed, including conceptual synthesis, model design and expert validation, implementation, and evaluation. Participants were 19 second-year pre-service science teachers from a university in Bangkok. Research instruments included a 40-item AI competency assessment and an instructional innovation evaluation rubric. Paired-sample t-test results indicated statistically significant pre–post difference across all AI competency dimensions, with large effect sizes (Cohen’s d = 0.82–1.59), reflecting notable within-group changes observed within the EDL–DLE learning context. The instructional innovation lesson plans were evaluated as generally strong across multiple dimensions, particularly in learner-centered pedagogy, creativity, and collaboration, while relatively lower performance was observed in appropriate AI technology selection and ethical use. Overall, the findings provide preliminary evidence supporting the feasibility of the EDL–DLE model as an exploratory instructional approach for fostering foundational AI-related pedagogical competencies in pre-service science teacher education
Contextual learning · Creativity · Educational technology · Experiential learning · Exploratory research · Instructional design · Learning Sciences · Science education · Science learning · Teacher education · AI in Service Interactions · Educational Games and Gamification · Teaching and Learning Programming
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Experiential learning – a systematic review and revision of Kolb’s model
Calculating and reporting effect sizes to facilitate cumulative science
Technological Pedagogical Content Knowledge
Curriculum Material Use in EFL Classrooms
What’s Next for Feedback in Writing Instruction? Pre-Service Teachers’ Perceptions of Assessment Practices and the Role of Generative AI
Open For All
A rubric for assessing teachers' lesson activities with respect to TPACK for meaningful learning with ICT
Digital Learning Ecosystem for Classroom Teaching in Thailand High Schools
| Citation velocity | historical |
|---|---|
| Highly cited | No |