Student Burnout and Mental Health in Higher Education During COVID-19: Online Learning Fatigue, Institutional Support, and the Role of Artificial Intelligence
Bibliographic record
Abstract
The COVID-19 pandemic significantly disrupted higher education. The sudden and profound transformations it necessitated had a direct and negative impact on higher education students, as evidenced by the widely reported instances of academic disengagement, decreased motivation, and lower performance. This was often due to student burnout caused by financial instability, mental health struggles, social isolation, and online learning fatigue. This qualitative narrative review, which analysed 38 peer-reviewed articles and adheres to SANRA, explores this burnout phenomenon, delving into learners' challenges during the pandemic, how their educational success was affected, and the universities’ strategies to mitigate the negative consequences. Understanding the link between burnout and academic success is crucial, as it will help inform future policies aimed at enhancing student resilience and learning outcomes. This review found that flexible academic policies, hybrid learning models, and mental health support services helped alleviate some of the challenges faced during the pandemic. In addition, AI-based tools such as chatbots and academic aids provided scalable emotional and academic support, particularly in online environments where traditional structures were limited. However, the long-term academic implications remain uncertain, despite the use of these learner management strategies to mitigate stress. Findings underscore the importance of continued research on sustainable digital and institutional support systems, including the integration of AI, in post-pandemic higher education.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".