Evaluating Students Acceptance of AI Chatbot to Enhance Virtual Collaborative Learning in Malaysia
Bibliographic record
Abstract
The pandemic COVID-19 has created a crisis in tertiary education sectors worldwide with significant impacts in Malaysia.It gives the challenge to students to cope with their new learning setup.However, with the help of technology such as AI chatbot, students can receive instant assistance in seeking and accessing information and limit the disruptions during online classes.Furthermore, the advancements in AI technology have led to improvements in natural language processing, enabling chatbots to engage in more natural interactions and provide better visual and audio representations.Therefore, the purpose of this study is to examine students' acceptance on the effectiveness of AI chatbots to solve virtual class issues.The factors involved in this process were identified and include perceived ease of use, perceived usefulness, and perceived security.A total of 376 responses were taken into this study, and the data were analyzed using SPSS software.The results indicated that higher education authorities should focus on the effectiveness of AI chatbot by its perceived ease of use which has the highest significance value followed by perceived usefulness and perceived security as the less significance value.Findings were proved by testing through Pearson correlation coefficient and multiple linear regression.University authorities should provide students with basic techniques for learning, as well as sufficient understanding and teaching about the system's capabilities, which can help students' confidence in and willingness to adopt the technology.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".