As my research has progressed, I have explored ideas, followed emerging developments, and engaged in ongoing writing, discussion, and reflection. Through this process, my initial thinking and direction have evolved. Six months ago, I outlined my first research problem (published here), noting that my work was spiralling, continually circling toward a core concept, shaped by a range of personal influences:

• Experience in educational technology
• Experience with the design process
• My curiosity about generative artificial intelligence (genAI)
• Our changing media landscape due to large language models
• What literature knowledge gaps exist? (yet feels speculative before diving in)
• What do I want to spend years researching?
• How my research may impact my future career prospects
• The risk of my research being outdated before it is even published (especially within a field moving as rapidly as genAI)

Six months ago, my research topic and problem concerned how the rise of large language models, capable of generating text indistinguishable from human writing, has disrupted writing-based assessment and increased the need for process-based assessment practices. The purpose of the research was to explore emerging process-based assessment practices to address research questions such as: How can the process of writing be captured? Which strategies can be used to assess human-AI collaboration in learning? Which strategies are agile to a variety of media ecologies?

As my research direction takes another twist and turn, the problem and theoretical perspective largely remain unchanged. What has changed is moving away from a focus on processes, often viewed as a sequential series of steps, maybe prescriptive and deterministic, toward a focus on the cognitive skills needed to meaningfully engage with them. This realization began in my literature review on Process-Based Writing Assessment, which outlined numerous opportunities for process-based strategies that felt nearly impossible to choose from and highlighted the dangers of prescribing a single process to learners. The numerous opportunities for process-based strategies also proved to be a roadblock in a design project to Assess Writing of Undergraduate Students in a GenAI World, where I landed upon the question: How can we empower learners to value and honour the process of learning? This angle supports learners and accepts that policing GenAI use is impossible to do fairly, is inequitable, is impractical, and does not primarily serve the learner. This brought me away from the domain of process and into cognitive abilities. I delved deeper into the world of design, particularly reading works by IDEO and the Stanford d.school. This ultimately led me to a validating article by Carter, the Stanford d.school’s Director of Teaching and Learning, in Let’s stop talking about THE design process, where this similar focus from process to ability is highlighted. Building on my progress so far, I present my updated topic, problem, purpose, question.

Topic

“the broad subject matter addressed by the study” (Creswell, 2012, p. 60)

The area of my research is in design-based learning, where learners are given a task to create something, from an essay to an engineering problem; they engage in design and, through this process, learn. This aligns with constructionism, which emphasizes learning through making tangible objects in the real world.

Specifically, there are numerous design mindsets people adopt when approaching and executing creative activities. These are cognitive skills that numerous world-leading designers highlight in one way or another. For example, Stanford d.school outlines eight core design abilities:

1. Navigate Ambiguity. To recognize and persist in the discomfort of not knowing, and develop tactics to overcome ambiguity when needed.
2. Learn From Others (People and Contexts). Empathizing with and embracing diverse viewpoints, testing new ideas with others, and observing and learning from unfamiliar contexts.
3. Synthesize Information. To make sense of information and find insight and opportunity within.
4. Experiment Rapidly. To quickly generate ideas – whether written, drawn, or built.
5. Move Beyond Concrete and Abstract. Understanding stakeholders as well as zooming in and expanding on product features.
6. Build and Craft Intentionally. Thoughtful construction: showing work at the most appropriate level of resolution for the audience and desired feedback.
7. Communicate Deliberately. To form, capture, and relate stories, ideas, concepts, reflections, and learnings to the appropriate audiences.
8. Design Your Design Work. This meta ability is about recognizing a project as a design problem and then deciding on the people, tools, techniques, and processes needed to tackle it.
   
   from Carter (2016), the Stanford d.school’s Director of Teaching and Learning, in Let’s stop talking about THE design process.

Problem

“a general educational issue, concern, or controversy addressed in research that narrows the topic” (Creswell, 2012, p. 60)

GenAI offers new opportunities for learners to both complement and shortcut their design work and learning. Therefore, there is a pressing need to understand how human–genAI collaboration shapes their practice.

Education does not occur in a vacuum. Shifts in the broader media ecology affect the how, the why, and the what we teach and learn. GenAI has made rapid advancements in creating media, including images, audio, video, and text. Specifically, LLMs can instantly generate text indistinguishable from human writing (Nikolic et al., 2024; Suchman, 2023) and have disrupted numerous cognitive tasks. About 80% of learners use genAI-powered tools in their education (Chung et al., 2026). It is important for learners to engage with these powerful tools and use them in ways that support, rather than hinder, learning.

This problem can be viewed through the lens of sociotechnical systems theory, in which social, technical, and environmental factors are inherently interdependent, in continuous interplay and redesign (Pasmore et al., 2019). A disruptive technology, such as genAI, demands a rebalancing of the system. Then, delving deeper into the educational problem of thinking and learning, the theory of distributed cognition views the system as a thinking entity. Therefore, competency is the ability to engage with and manage a distributed system of individuals, environments, and tools (Fawns & Schuwirth, 2024; Pea, 1993).

While human–genAI collaboration is already occurring and likely to shape the future of work (Eaton, 2023), its adoption is largely self-directed with little guidance or instruction from institutional resources (Chung et al., 2026). Better understanding how learners use genAI can lead to more effective pedagogical strategies and recommendations surrounding genAI use.

Purpose

“the major intent or objective of the study used to address the problem” (Creswell, 2012, p. 60)

 This research aims to explore how learners collaborate with genAI to support their design tasks, with particular attention to the cognitive work they delegate and its impact on learning. Design abilities will be examined as a framework to ground these cognitive processes and explore their relationships with genAI use.

Question

“narrows the purpose into specific questions that the researcher would like answered or addressed in the study” (Creswell, 2012, p. 60)

Narrowing the purpose into specific questions:

• For which tasks do learners utilize genAI?
• How is genAI used for these tasks?
• Why is genAI used in these tasks?
• Is there a relationship between a learner’s design mindset profile and their GenAI practices?
• Is a learner’s design mindset profile, and/or GenAI practices, a predictor of academic achievement?

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References

Carter, C. (2016). Let’s stop talking about THE design process. Medium. https://medium.com/stanford-d-school/lets-stop-talking-about-the-design-process-7446e52c13e8

Chung, J., Henderson, M., Slade, C., Liang, Y., Pepperell, N., Corbin, T., Walton, J., Yu, A. S., Bearman, M., Shum, S. B., Fawns, T., McCluskey, T., McLean, J., Oberg, G., Seligmann, A., Shibani, A., Bakharia, A., Lim, L.-A., & Matthews, K. E. (2026). The use and usefulness of GenAI in higher education: Student experience and perspectives. Computers and Education Open, 100347. https://doi.org/10.1016/j.caeo.2026.100347

Creswell, J. W. (2012). An Introduction to Educational Research. In Educational research: Planning, conducting, and evaluating quantitative and qualitative research (Fourth Edition). Pearson Education, Inc.

Eaton, S. E. (2023). Postplagiarism: Transdisciplinary ethics and integrity in the age of artificial intelligence and neurotechnology. International Journal for Educational Integrity, 19(1), 23, s40979-023-00144–1. https://doi.org/10.1007/s40979-023-00144-1

Fawns, T., & Schuwirth, L. (2024). Rethinking the value proposition of assessment at a time of rapid development in generative artificial intelligence. Medical Education, 58(1), 14–16. https://doi.org/10.1111/medu.15259

Nikolic, S., Sandison, C., Haque, R., Daniel, S., Grundy, S., Belkina, M., Lyden, S., Hassan, G. M., & Neal, P. (2024). ChatGPT, Copilot, Gemini, SciSpace and Wolfram versus higher education assessments: An updated multi-institutional study of the academic integrity impacts of Generative Artificial Intelligence (GenAI) on assessment, teaching and learning in engineering. Australasian Journal of Engineering Education, 29(2), 126–153. https://doi.org/10.1080/22054952.2024.2372154

Pasmore, W., Winby, S., Mohrman, S. A., & Vanasse, R. (2019). Reflections: Sociotechnical Systems Design and Organization Change. Journal of Change Management, 19(2), 67–85. https://doi.org/10.1080/14697017.2018.1553761

Pea, R. D. (1993). Practices of distributed intelligence and designs for education. In G. Salomon (Ed.), Distributed cognitions: Psychological and educational considerations (pp. 47–87). Cambridge University Press.

Suchman, L. (2023). The uncontroversial ‘thingness’ of AI. Big Data & Society, 10(2), 20539517231206794. https://doi.org/10.1177/20539517231206794