Prompt Literacy as an Enhancer of Students' Academic Writing in Higher Education Institutions: A Systematic Literature Review
Bibliographic record
Abstract
Prompt literacy has emerged as a pivotal concept in academic writing, particularly within higher education. This systematic literature review (SLR) critically examines and synthesizes research conducted between 2020 and 2025 on using prompting strategies to enhance academic writing among university students. The review aims to identify the types of prompts employed, evaluate their pedagogical effectiveness, explore the contexts of their implementation, and assess the outcomes associated with their use. The SLR followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, encompassing four key stages: data search and collection, selection criteria, data extraction, and data analysis. A two-stage screening process—pre-screening and final eligibility selection—was applied to ensure the inclusion of relevant studies. Findings reveal that 40.5% of the reviewed studies (n=17) adopted a mixed methods research design, reflecting a growing trend toward integrating qualitative and quantitative insights. A central theme across the literature is the critical role of prompt formulation in maximizing the benefits of AI technologies for academic writing. Effective prompts significantly enhanced students' engagement, critical thinking, and writing proficiency. The review also highlights the CLEAR framework as a guiding model for implementing prompting strategies, with implications spanning pedagogical practices, technological integration, and institutional policy development. This review underscores the transformative potential of well-designed prompting strategies in higher education. It calls for a more nuanced understanding of prompt literacy as a foundational skill in the digital age, advocating for targeted interventions and policy support to foster its development. The findings contribute to the growing knowledge of academic writing enhancement and provide actionable insights for educators, instructional designers, and policymakers.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".