AI-Powered Learning Activities for Enhancing Student Competencies in Electronic Media Production: A Classroom Action Research
Bibliographic record
Abstract
Artificial intelligence’s (AI) quick development has had a big impact on education, especially in industries like electronic media production that call for both creative expression and practical abilities. In a classroom action research setting, this project investigates how to use AI-powered learning activities to improve students’ proficiency in producing electronic media. We conducted the research with 15 undergraduate students from Kasetsart University’s Digital Technology for Education (DTE) program. Students utilized AI-driven tools, including StoryboardThat, Canva, Microsoft Bing, CapCut, and Adobe Premiere Pro, to complete four media production tasks: storyboard, 3D objects, infographics, and multimedia. The findings revealed that AI tools, particularly user-friendly platforms like Canva and CapCut, significantly improved students’ technical skills and creative capabilities, with high satisfaction reported for tools that simplified complex tasks. Reflective journals indicated enhanced efficiency, creativity, and self-assessment among students. However, tools with complex interfaces, such as Adobe Premiere Pro, presented challenges, underscoring the need for targeted instructional support. This study highlights the potential of AI in fostering both technical proficiency and creative autonomy in media production education while also identifying areas for future improvement in AI tool accessibility and usability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".