The study, conducted by Muhammad Shaban Rafi of Riphah International University and Lubna Khalil of the University of the Punjab, applied the Artificial Intelligence Assessment Scale developed by Perkins and colleagues in 2024 to evaluate the appropriate role of generative AI across the successive stages of dissertation writing. Rather than treating AI as either a threat or a miracle, the framework allowed the researchers to distinguish between tasks that can be fully delegated to machines, tasks that require a human–AI partnership, and tasks that must remain exclusively human. The results challenge both the alarmist narrative that AI will hollow out doctoral scholarship and the utopian claim that it can write a dissertation on command.

Where the technology shone most clearly was in the mechanics of writing. Participants reported that generative AI could be used without reservation to improve grammar, sentence structure, and overall coherence, enhancing the clarity and efficiency of their prose. For many doctoral students, particularly those writing in a second language, this mechanical support removed a persistent barrier that had previously consumed hours of revision. By offloading surface-level editing to the machine, students said they could redirect their energy toward deeper intellectual engagement with their arguments, literature, and data, a shift that supervisors in the study viewed as a legitimate pedagogical gain rather than a shortcut.

The second major application involved data analysis. Doctoral candidates described using generative AI to help analyze large qualitative datasets by defining a coding frame, identifying recurring trends, and conducting sentiment analyses. In the social sciences and humanities, where interviews, open-ended survey responses, and archival texts can generate overwhelming volumes of material, the ability of large language models to propose initial coding categories and flag patterns offered a practical starting point. Crucially, the participants did not describe the AI as replacing their analytical judgment. Instead, the machine-generated coding frames served as provisional scaffolding that researchers then tested, refined, and validated against their own close reading of the data.

A third productive territory was argument structuring. Students reported using generative AI to organize literature reviews, suggest logical arrangements of sentences and paragraphs, and generate counterarguments that stress-tested their claims. The capacity of these models to simulate an opposing viewpoint proved especially valuable in the humanities, where a dissertation’s strength often depends on anticipating objections. By prompting the system to challenge a thesis statement or identify gaps in a literature synthesis, candidates effectively gained a tireless sparring partner available at any hour, one that could surface alternative framings the writer had not considered.

Yet the enthusiasm had sharp limits. Participants were emphatic that generative AI should be limited or prohibited in areas demanding critical reasoning, originality, and cultural context. The core intellectual contributions of a dissertation, formulating research questions, interpreting findings within their disciplinary traditions, and producing genuinely novel insights, were judged to require human intelligence at their center. Supervisors in particular warned that AI-generated content may lack accuracy and contextual depth, producing text that reads fluently but fails under scholarly scrutiny. The study highlights the well-documented problem of hallucination in natural language generation, in which models confidently produce plausible-sounding but false claims, a hazard especially dangerous in academic contexts where fabricated citations or mischaracterized sources can constitute serious misconduct.

This is where the concept of prompt literacy enters the picture. The researchers argue that the quality of output from generative AI is not a fixed property of the technology but a function of how skillfully the user frames the request. Prompt engineering has emerged in recent years as a recognized digital competence, with studies describing it as a new twenty-first-century skill and cataloging systematic techniques, from chain-of-thought prompting, which guides models through explicit reasoning steps, to retrieval-augmented generation, which grounds model outputs in external source documents. The study positions prompt literacy as an essential component of broader AI literacy, encompassing the ability to specify role, context, constraints, and desired output format in a prompt, and to iteratively refine instructions when the first response falls short.

The pedagogical implications are significant. If the difference between a vague, hallucination-prone response and a precise, useful one lies in the prompt, then universities face a choice about whether to teach these skills explicitly or leave students to learn by trial and error. The study responds by providing a scale for the appropriate use of generative AI at each stage of doctoral writing, along with ready-to-use prompts developed as part of the research. Such resources suggest a model of AI integration in which institutions define clear boundaries, students develop the technical and critical competence to work within them, and assessment practices evolve to distinguish between acceptable assistance and academic dishonesty. The ethical dimension is central: the study received ethical approval under professional research ethics codes, and its framework is explicitly designed for the ethical integration of AI in educational assessment.

The findings arrive amid a rapidly expanding body of research on generative AI in higher education, including studies of students’ prompt patterns, hermeneutic approaches to prompt optimization, and surveys of hallucination in language models. What distinguishes this study is its focus on the doctoral dissertation, the genre in which the stakes of originality are highest and the tension between efficiency and authenticity most acute. By grounding its conclusions in the lived experience of students and supervisors rather than speculation, it offers a pragmatic middle path: generative AI as a powerful tool for mechanics, data handling, and argument testing, governed by human judgment, validated at every step, and wielded by researchers who understand both what to ask and what the machine cannot be trusted to answer.

For the current generation of doctoral candidates, the message is double-edged. The technology can genuinely save time and elevate the quality of dissertation writing, but only for those who invest in the literacy to use it well. As generative AI becomes embedded in the infrastructure of research, the study suggests that the most important qualification for the twenty-first-century scholar may not be the ability to write without AI, or even with it, but the ability to converse with it critically, skeptically, and skillfully, keeping human intelligence, as the authors insist, at the core of the scholarly enterprise.

Subject of Research: The applications, limitations, and prompt literacy requirements of generative AI in doctoral dissertation writing

Article Title: Generative AI in Doctoral Dissertation Writing: Applications, Limitations, and the Need for Prompt Literacy

Article References: Generative AI in Doctoral Dissertation Writing: Applications, Limitations, and the Need for Prompt Literacy. (n.d.). https://doi.org/10.1007/s44366-026-0088-9

Image Credits: AI Generated

DOI: 10.1007/s44366-026-0088-9

Keywords: generative AI, doctoral dissertation writing, prompt literacy, AI Assessment Scale, human–AI collaboration, AI literacy, higher education, academic writing, prompt engineering, hallucination, ethical use of AI, social sciences and humanities