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AI-assisted academic writing still operates in a murky area where ethical boundaries are not always clear.

This is especially true in dissertation writing.

AI can be a powerful support tool for doctoral students, especially L2 doctoral students who already navigate the heavy demands of academic English and the emotional pressure of producing a long scholarly text.

For many of these students, AI is not simply a shortcut. It can function as a language support tool, a brainstorming partner, and sometimes a source of confidence when writing in a second language.

But this also creates difficult questions, ones that we all need to grapple with including:

When does AI support become AI dependence?
When does language improvement become text generation?
When does help with clarity become unacknowledged co-writing?
And how should dissertation committees evaluate work shaped partly by GenAI tools?

These questions cannot be left for students to figure out alone.

The first responsibility lies with institutions. Universities need clear guidance and well-articulated policies that define acceptable and unacceptable AI use in dissertation writing.

Students should not have to guess where the ethical line is. Policies should explain what can be used, what must be disclosed, how disclosure should be written, and how data privacy should be protected.

The second responsibility lies with teachers, supervisors, and dissertation committees. Students need more than warnings about plagiarism. They need models of ethical AI use and explicit conversations about what AI can support and what it must not replace. They also need to understand why authorship, originality, evidence, and accountability key cornerstones in academic writing.

This is what I found interesting in a recent study by Hoomanfard and Shamsi (2025), which examined L2 doctoral students’ use of GenAI in dissertation writing.

The authors interviewed 54 doctoral students writing dissertations in English as a second language. The findings showed that students used GenAI for 18 different purposes, grouped into three broad categories:

1. Exploration
Students used GenAI to brainstorm ideas, explore subject-area knowledge, understand research methods, examine rhetorical structures, clarify vocabulary, learn citation practices, and improve grammar.

2. Confirmation
Students used GenAI to check what they had already written: grammar, vocabulary, citations, coherence, methodology, and subject-area content.

3. Ex*****on
Students also used GenAI to produce or transform material: formatting text, summarizing sources, writing sentences or paragraphs, creating visuals, and even analyzing data.

And the interesting part in this study is that many students were unsure where legitimate AI use ends and plagiarism begins. Some believed AI use is ethical as long as the main ideas are theirs. Others saw any AI involvement as unethical. Even more revealing, most participants said they would not acknowledge their AI use in the dissertation.

Some felt their use was too minor to mention. Others feared that supervisors, committee members, or future employers would judge them negatively if they disclosed AI assistance.

This tells us something important. Silence around AI use does not always mean students are trying to cheat. Sometimes it means they are confused, unsupported, or afraid of being misread.

The study also found that students want training. They want to learn which AI tools are suitable for specific tasks, how to write better prompts, how to avoid AI-giarism, when and how to disclose AI use, and how to protect their data and unpublished research.

That is where the conversation needs to go, toward practical, ethical, and explicit guidance.

Link in the first comment!

Reference:
Hoomanfard, M., & Shamsi, Y. (2025). Generative AI in dissertation writing: L2 doctoral students’ self-reported use, AI-giarism, and perceived training needs. Journal of English for Academic Purposes, 78, 101570. https://doi.org/10.1016/j.jeap.2025.101570

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Founder & Educational Technologist

Over the past few months, I’ve been sharing resources to help educators think more critically about AI in teaching.

This one introduces a human-centered approach to feedback, combining peer review with AI while keeping student agency and reflection at the core. A useful reminder that AI works best when it supports human judgment.

Everything you need to know about Microsoft copilot for education!

Link in the first comment.

AI Integration Tips for Teachers!

In this guide, I share four practical tips for integrating AI into your teaching, collected from my own classroom experience, from years of writing about educational technology, and from the research literature on AI in education.

The argument running through the whole guide is that pedagogy has to come first. Your learning goals, the evidence you need from students, the activities that get them there.

That's the logic of backward design, and it applies to AI integration the same way it applies to any other instructional decision. Wiggins and McTighe (2005) laid this out years ago, and it still holds.

AI belongs in the planning sequence after goals and evidence are already clear.

The guide covers four areas. It opens with building your AI pedagogy, with a self-assessment rubric you can use to see where you are and where you want to grow.

From there it moves to co-creating a classroom AI agreement with your students, because an agreement students helped shape works better than a policy handed down.

The third section tackles assessment, which I think is a validity problem before it's a cheating problem, and it includes strategies and a set of design questions to pressure-test your assignments.

The final section gives you an evaluation rubric for AI tools, built from frameworks by UNESCO, the OECD, aiEDU, and ISTE.

Every tip comes with a table, rubric, or template you can pick up and adapt. I wanted this to be something you come back to, not something you read once and shelve.

The guide is free and licensed under Creative Commons.

Link in the first comment!



References

Wiggins, G., & McTighe, J. (2005) Understanding by design (2nd ed.). Alexandria, VA: association for supervision and curriculum development ASCD