Popal H1
1UNSW, Sydney, Australia
Biography:
Dr Heela Popal is a Research Data Specialist in Research Technology Services at the University of New South Wales (UNSW), where she supports researchers in adopting best-practice research data management, FAIR data principles, and digital research infrastructure. She holds a PhD in Government and International Relations from the University of Sydney and has an extensive background in higher education teaching and research. Heela was awarded the Dean’s Citation for Excellence in Teaching and served as a Postgraduate Teaching Fellow at the University of Sydney. She is also an Adjunct Senior Lecturer in the Faculty of Arts, Design and Architecture at UNSW. Her expertise includes qualitative data analysis, research data management, and enabling impactful, data-driven research through innovative technologies and collaborative practices.
This keeps the focus on your current role while highlighting your qualitative research expertise, which is particularly relevant for eResearch audiences interested in both qualitative and quantitative research methods.
Abstract:
Generative AI is rapidly reshaping research practices across the Humanities, Arts, and Social Sciences (HASS), offering new possibilities for idea generation, literature exploration, project planning, drafting support, and communication. Yet its use also raises urgent questions about authorship, academic integrity, critical thinking, disciplinary judgement, and the potential outsourcing of analytical labour. For HASS researchers, whose work often depends on interpretation, context, argumentation, and reflexive critique, the challenge is not simply whether to use AI, but how to use it without surrendering the intellectual work that defines scholarly practice.
This presentation examines safe and responsible AI use in HASS research through the principle of “remaining the author.” It argues that AI should be positioned as a research support tool rather than a substitute researcher, assisting with workflow efficiency while leaving critical interpretation, methodological decision-making, ethical reflection, and analytical reasoning under human control. Drawing on work developing AI-informed HASS research workflows, the presentation outlines practical strategies for integrating AI across the research lifecycle while maintaining transparency, verification, accountability, and researcher agency.
The presentation also addresses key risks, including hallucinated sources, bias, overreliance, loss of critical engagement, and unclear authorship boundaries. It proposes a human-centred workflow model that helps researchers distinguish between appropriate AI assistance and inappropriate intellectual delegation. By reframing AI as a tool for supporting, rather than replacing, scholarly judgement, this presentation contributes to current debates on responsible AI adoption and offers practical guidance for researchers, educators, and institutions seeking to protect the critical and analytical foundations of HASS research.
