The literature review is one of the most intellectually demanding components of academic writing. For undergraduates, synthesizing a body of scholarship requires not only comprehension of individual studies but also the ability to map connections, highlight gaps, and construct critical arguments. While such tasks are foundational for cultivating scholarly literacy, students frequently struggle with information overload, inadequate structuring, and limited critical evaluation skills.
In parallel, the rise of large language models (LLMs), particularly ChatGPT, has transformed possibilities for writing assistance in higher education. Yet, the effectiveness of LLMs hinges significantly on how prompts are designed. This study asks: how can structured prompt frameworks enhance the quality of AI-generated literature reviews, and what implications does this have for undergraduate learning? By focusing on prompt design and systematic evaluation, we contribute empirical evidence to the growing field of AI-assisted pedagogy.
ChatGPT, developed by OpenAI, has been widely adopted as a tool for text generation, summarization, and dialogue-based interaction (OpenAI, 2023). In educational contexts, it has been leveraged for tasks ranging from grammar correction to argumentative essay support (Kasneci et al., 2023). Several studies highlight its ability to scaffold student learning by providing real-time feedback and alternative phrasings (Gao et al., 2023). Nonetheless, concerns persist over accuracy, factual reliability, and the ethical risks of over-reliance (Stokel-Walker, 2023).
The literature review is not merely a descriptive catalog of prior work; it demands synthesis, critique, and the positioning of one’s research within a scholarly conversation (Hart, 2018). Undergraduate students often lack the conceptual and rhetorical tools to move beyond summary. Hyland (2019) stresses that this stage of writing is crucial for cultivating academic voice, yet empirical evidence shows that novice writers overemphasize reporting at the expense of critical evaluation.
Prompt engineering has emerged as a pivotal method for enhancing LLM performance (Brown et al., 2020). Structured prompts—those that specify tasks, roles, and output formats—are shown to yield more accurate and logically coherent results compared to open-ended instructions (Liu et al., 2023). In education, prompt frameworks may function not only as technical input guides but also as cognitive scaffolds, shaping how students conceptualize and engage with academic genres (Wu et al., 2023).
Scholars increasingly emphasize the need for systematic evaluation of AI-generated academic texts. Metrics often include factual accuracy, logical coherence, stylistic appropriateness, and alignment with disciplinary conventions (Zhang et al., 2023). Mixed-method evaluations that combine expert ratings, textual analyses, and learner feedback provide a holistic view of how AI can augment writing (Feng & Boyd-Graber, 2022).
Although prior work establishes the potential of LLMs and prompt engineering, little attention has been given to literature review generation specifically. Most evaluations focus on essay writing or summarization, leaving unexplored how AI can support synthesis-driven academic genres. This study fills that gap by designing and empirically testing prompt frameworks tailored to literature reviews, thereby contributing methodological insights and pedagogical implications.
This study employs a mixed-methods design integrating experimental generation tasks, expert evaluations, and student feedback. The primary objective is to examine how different prompt frameworks affect the quality of ChatGPT-generated literature reviews.
Sixty undergraduate students enrolled in academic writing courses at a comprehensive university participated. They were divided into three groups, each working with a distinct prompt framework. All participants had prior exposure to basic research methods but limited experience in writing extended literature reviews.
Three frameworks were developed:
Baseline Open Prompt: “Write a literature review on [topic].” No structural guidance provided.
Structured Prompt: Guidance specifying introduction, thematic organization, methodological comparisons, and research gaps.
Critical Prompt: Includes structured elements plus explicit requests for evaluation, comparative analysis, and highlighting controversies.
Each framework was tested across three domains: education, natural language processing, and social sciences.
Each group generated a 1,000-word literature review with ChatGPT under supervision. Outputs were anonymized and submitted for expert evaluation. In addition, students completed reflective surveys and participated in focus group discussions on their experiences.
Four dimensions of quality were operationalized:
Academic Rigor: Correct use of terminology, citation plausibility, and disciplinary conventions.
Accuracy: Factual reliability, avoidance of fabricated references.
Logical Coherence: Structural organization, flow of argument, clarity of thematic transitions.
Readability: Language fluency, stylistic appropriateness, accessibility for undergraduate audiences.
Three independent raters with expertise in academic writing pedagogy scored each text on a 5-point Likert scale across dimensions. Inter-rater reliability (Cohen’s kappa) was calculated to ensure consistency.
Quantitative data were analyzed using ANOVA to identify significant differences between groups. Qualitative student reflections were coded thematically to capture perceptions of cognitive scaffolding, confidence building, and limitations. Representative excerpts were integrated to triangulate findings.
All participants provided informed consent. The study emphasized responsible use of AI, clarifying that ChatGPT serves as a writing assistant rather than a substitute for student authorship. Outputs were used solely for research and anonymized prior to analysis.
ANOVA results revealed significant differences across prompt conditions (p < 0.01). Critical prompts produced the highest scores in academic rigor (M = 4.2), logical coherence (M = 4.3), and readability (M = 4.5). Structured prompts outperformed baseline prompts but lagged behind critical prompts in depth of evaluation. Accuracy scores remained moderate across all conditions, reflecting persistent challenges with fabricated references.
Student reflections indicated that structured prompts clarified expectations and reduced anxiety. Critical prompts, in particular, encouraged students to think beyond description:
“It made me realize that a literature review is not just a summary but a conversation among scholars.”
However, participants also noted frustrations with inaccuracies, especially in references. Many emphasized the need for integration with validated databases to ensure factual correctness.
Findings suggest that prompt frameworks can function as cognitive scaffolds, guiding students toward higher-order writing skills. By explicitly embedding evaluation tasks, critical prompts align with pedagogical goals of fostering critical thinking and synthesis. Nevertheless, the persistence of factual errors highlights the need for hybrid approaches that combine AI assistance with human verification and academic training.
The study advances understanding of prompt engineering as not only a technical intervention but also a pedagogical tool. It underscores that AI’s effectiveness in education depends on how human instructors design interactions to scaffold student cognition.
This study examined the impact of prompt frameworks on ChatGPT’s ability to generate literature reviews in undergraduate contexts. Findings indicate that structured and critical prompts significantly enhance output quality, particularly in logic, organization, and critical engagement. Student reflections further confirm the pedagogical value of prompts as cognitive scaffolds. However, challenges remain regarding factual reliability and reference authenticity, limiting the extent to which AI-generated reviews can be used without verification.
The research contributes to both methodological and pedagogical debates. Methodologically, it establishes a systematic framework for evaluating AI-generated academic texts. Pedagogically, it demonstrates how prompt engineering can foster critical thinking in students. Future work should integrate AI systems with authoritative databases and explore adaptive prompt frameworks tailored to disciplinary conventions. Ultimately, the findings highlight a dual lesson: while ChatGPT has transformative potential for academic writing, its effective use depends on deliberate human-AI collaboration grounded in both technical design and educational practice.
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