Special Issue: Artificial Intelligence in Open and Distributed Learning: Does It Facilitate or Hinder Teaching and Learning?
Bibliographic record
Abstract
Artificial intelligence (AI) is a rapidly evolving field with the potential to revolutionize various aspects of education, especially in open and distributed learning, including distance education, hybrid learning, and blended learning.AI can transform curriculum design, content delivery, assessment, feedback, learner support, and learning analytics (Chen et al., 2020).AI offers personalized and adaptive learning paths based on learners' preferences, needs, goals, and performance, enhancing their educational experience (Holmes et al., 2023).It also provides timely feedback and guidance, fostering engagement and motivation.AI creates interactive and immersive learning environments, such as games, simulations, and virtual reality, sparking learners' interest and involvement.It promotes social and collaborative learning by facilitating communication and cooperation among learners, instructors, and resources (Holmes et al., 2023).Additionally, AI optimizes various tasks like content creation, grading, assessment, and learning analytics, improving the quality and efficiency of education processes.However, AI in education also raises significant challenges and risks.Ethical, legal, social, pedagogical, and technical issues need consideration (Chen et al., 2022;Ouyang & Jiao, 2021).For example, maintaining AI applications' quality, validity, reliability, and fairness remains crucial.Protecting learners' and instructors' privacy, security, and autonomy in AImediated learning contexts is also vital (Holmes et al., 2023).Moreover, fostering critical thinking, creativity, and human values in AI-enhanced learning experiences is crucial.Lastly, addressing the digital divide and potential marginalization of learners and instructors without access to or skills in AI technologies is paramount (Holmes et al., 2023).Overall, AI has immense potential in education, but its responsible and informed implementation is necessary to ensure its benefits are maximized while mitigating potential risks.Given the above points, this special issue aims to explore the opportunities and challenges of AI in open and distributed learning, including distance education, hybrid learning, and blended learning, from multiple perspectives.We invited
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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 | 0.013 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.000 | 0.003 |
| 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".