Adaptive Digital Project-Based Learning Model with Artificial Intelligence Technology to Promote Digital Creations
Bibliographic record
Abstract
This research aims to develop an adaptive digital project-based learning model enhanced with artificial intelligence technology to facilitate the creation of digital content. A systematic approach was employed, divided into three phases: 1) study and synthesis of conceptual frameworks to understand the elements and relationships of related concepts, 2) design of a learning model that integrates artificial intelligence technology with digital project-based learning, and 3) evaluation of the designed model by nine experts. The participants are nine experts with knowledge and abilities in teaching and learning design and development from various institutions, selected through purposive sampling. The research findings found that 1) the developed model consists of four main components: adaptation, digital, project, and learning, with a seven-step learning process (identify topics and analyze learning needs, collect and analyze data using digital tools, design digital project plans, implement learning and create personized learning pathways, create creative digital works, share online, and reflect for evaluation) focused on adapting to learners' needs, and 2) the overall suitability of the A-DPL model with artificial intelligence technology is at highest level (mean = 4.70, SD = 0.22). This indicates that the learning model is characterized by the integration of project-based learning with digital technology and artificial intelligence, providing learners with a personalized learning experience while promoting creative thinking and digital skills that can effectively respond to educational challenges in the 21st century.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".