Development of an Artificial Intelligence Chatbot-Integrated Learning Platform to Enhance Information, Media, and Technology Literacy Skills for 21st-Century Learners in Distance Learning System
Bibliographic record
Abstract
The objectives of this research were to: 1) develop an artificial intelligence chatbot-integrated learning platform to enhance information, media, and technology literacy skills for 21st-century learners in distance learning system, and 2) study the effects of using this platform to enhance these skills. The sample group consisted of 32 undergraduate students from Sukhothai Thammathirat Open University in Thailand, selected through voluntary sampling. The research tools included: 1) the AI chatbot-integrated learning platform designed to enhance information, media, and technology literacy skills, and 2) pre-tests and post-tests assessing these literacy skills. Data were analyzed using percentages, means, standard deviation, t-tests, and content analysis. The research findings were as follows: 1) The development of the artificial intelligence chatbot-integrated learning platform to enhance information, media, and technology literacy skills for learners in distance learning system included four key components: (1) input, with six sub-components—(1.1) learner analysis, (1.2) content analysis, (1.3) learning platform, (1.4) artificial intelligence chatbot, (1.5) personnel, and (1.6) an information, media, and technology literacy test for learners in the distance education system; (2) process; (3) output; and (4) feedback. The AI chatbot’s implementation comprised three steps: (1) pre-learning, (2) self-learning, and (3) self-evaluation. The quality of the AI chatbot-integrated learning platform, as evaluated by experts, was rated at the highest level. 2) The study on the use of the AI chatbot-integrated learning platform revealed that the sample group of 32 students had significantly higher post-test scores compared to their pre-test scores, with statistical significance at the.05 level (t = -15.90, p = .00). Based on the analysis of users’ attitudes and abilities, it was found that the AI chatbot learning platform is effective as a tool for educational recommendations in distance education. The AI chatbot learning platform can also accurately analyze learners’ questions and provide precise answers that meet their needs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".