Rethinking research methods teaching and assessment in the age of AI

How game-based assessment can support learning, integrity, and engagement

Research methods has long been one of the most challenging areas to teach in business schools. Concepts such as ontology, epistemology and methodological alignment are abstract, often leaving students overwhelmed and struggling to connect theory with practice (Stevens, 2022; Kolb, 2013; Morris, 2020).

At the same time, the rapid rise of generative AI is reshaping higher education. Students are increasingly using AI tools to produce written work, raising important questions about academic integrity and, more fundamentally, what our assessments are actually measuring (Awdry and Ives, 2022; HEPI, 2025).

If traditional assessments can be completed or replicated by AI, then simply detecting misuse is not enough. The real challenge is redesigning assessment so that it captures learning, not just output.

Moving from detection to design

Much of the sector conversation has focused on AI detection tools. However, emerging evidence suggests these are unreliable and risk both missing misuse and incorrectly flagging genuine student work (Erol et al., 2025; Hadra et al., 2026).

More importantly, AI introduces a deeper issue: assessments may begin to measure students’ ability to use AI, rather than their own understanding. This challenges the validity of assessment itself (OECD, 2026).

As a result, the focus is shifting. Rather than asking “How do we catch misuse?”, the more important question becomes: How do we design assessment that makes misuse less relevant?

A different approach: learning through doing

One response to this challenge is The Philosopher’s Tale ©, a game-based approach to teaching and assessing research methods.

The idea is simple: instead of asking students to write about research design, ask them to do it in real time, collaboratively, and with immediate feedback.

The game takes students through the full research journey, from idea generation to methodological justification. Using visual boards structured around key stages, such as ontology, epistemology and methods, students progress step-by-step, making and defending decisions as they go.

This approach reflects principles of experiential learning, where knowledge is developed through experience, reflection, and application (Kolb, 2013), and addresses critiques that experiential approaches often lack structured reflection (Morris, 2020).

Why this matters for business schools

This approach aligns closely with what we know about effective learning in business education. First, it supports experiential learning by actively engaging students in decision-making and reflection (Kolb and Kolb, 2018). Second, it aligns with authentic assessment, where students apply knowledge in realistic contexts. Such approaches are associated with improved engagement, satisfaction, and employability-related skills when well designed (Ashford-Rowe et al., 2014; Villarroel et al., 2018).

For business schools focused on developing critical thinkers and decision-makers, this provides a strong pedagogical fit.

Building AI-resilient assessment

Perhaps most importantly, this approach is more resilient to AI misuse.

Because assessment is embedded within interaction, discussion, reasoning, and real-time responses, it cannot easily be outsourced or replicated. This reflects wider recommendations to move towards process-based and dialogic assessment approaches (Kofinas et al., 2025; Rudolph et al., 2023).

Rather than trying to control AI use, the focus becomes designing learning experiences where genuine understanding is required and visible.

What impact does it have?

Early use of The Philosopher’s Tale © suggests significant benefits. Students report increased confidence, improved understanding, and higher engagement compared to traditional approaches.

These outcomes are consistent with wider evidence that well-designed experiential and authentic learning approaches can enhance student engagement and learning outcomes (Irons and Elkington, 2021; Taguma and Barrera, 2019). 

Practical implications for educators

For educators, the key takeaway lies both in the game itself as an innovative pedagogical tool and in the underlying design principles it embodies. This includes:

• assessing process rather than just outputs

• embedding dialogue and real-time reasoning

• supporting reflection and feedback loops

• designing for authentic, applied decision-making

These approaches can be adapted across levels from undergraduate to doctoral study by adjusting complexity and expectations.

Looking ahead

The rise of AI presents both a challenge and an opportunity. It pushes us to rethink how we design learning and assessment in higher education.

Game-based, experiential approaches such as The Philosopher’s Tale © demonstrate how assessment can move beyond static outputs towards dynamic, interactive, and meaningful learning experiences.

In doing so, they offer a pathway towards assessment that is not only more engaging and effective, but also more resilient in an AI-augmented educational landscape.

References:

Ashford-Rowe, K., Herrington, J. and Brown, C., (2014). Establishing the critical elements that determine authentic assessment. Assessment & Evaluation in Higher Education, 39(2), pp.205-222.

Awdry, R. and Ives, B. (2022) ‘International predictors of contract cheating in higher education’, Journal of Academic Ethics, 21, pp. 193–212. doi:10.1007/s10805-022-09449-1.

Erol G, Ergen A, Gülşen Erol B, Kaya Ergen Ş, Bora TS, Çölgeçen AD, Araz B, Şahin C, Bostancı G, Kılıç İ, Macit ZB, Sevgi UT, Güngör A. Can we trust academic AI detective? Accuracy and limitations of AI-output detectors. Acta Neurochir (Wien). (2025) Aug 7;167(1):214. doi: 10.1007/s00701-025-06622-4. PMID: 40773066; PMCID: PMC12331776.

Hadra, M., Cambridge, K. & Mesbah, M. Evaluating the accuracy and reliability of AI content detectors in academic contexts. Int J Educ Integr 22, 4 (2026). https://doi.org/10.1007/s40979-026-00213-1

HEPI, (2025), Student Generative AI Survey (2025). Oxford: HEPI. Available at: https://www.hepi.ac.uk/reports/student-generative-ai-survey-2025/ (Accessed: 29 March 2026)

Irons, A. and Elkington, S., (2021). Enhancing learning through formative assessment and feedback. Routledge.

Kofinas, A.K., Tsay, C.H‑H. and Pike, D. (2025) ‘The impact of generative AI on academic integrity of authentic assessments within a higher education context’, British Journal of Educational Technology, 56(6), pp. 2522–2549. doi:10.1111/bjet.13585.

Kolb, D.A., (2013). The process of experiential learning. In Culture and processes of adult learning (pp. 138-156). Routledge.

Kolb, A. and Kolb, D., (2018). Eight important things to know about the experiential learning cycle. Australian educational leader, 40(3), pp.8-14.

Morris, T.H., (2020). Experiential learning–a systematic review and revision of Kolb’s model. Interactive learning environments, 28(8), pp.1064-1077.

Organisation for Economic Co-operation and Development (OECD) (2026) OECD Digital Education Outlook 2026: Exploring Effective Uses of Generative AI in Education. Paris: OECD Publishing. Available at: https://doi.org/10.1787/062a7394-en

Rudolph, J., Tan, S. and Tan, S., (2023). ChatGPT: Bullshit spewer or the end of traditional assessments in higher education?. Journal of applied learning & teaching, 6(1), pp.342-363.

Stevens, M. (2022) ‘Gamification and experiential learning in research methods’, in Remenyi, D. (ed.) Innovation in Teaching Research Methodology. Reading: Academic Conferences International Limited, pp. 75–94.

Taguma, M. and Barrera, M., 2019. OECD future of education and skills 2030: Curriculum analysis. Dispon. Su Httpswww Oecd Orgeducation2030-Proj.--Learn. Pdf.

Villarroel, V., Bloxham, S., Bruna, D., Bruna, C. and Herrera-Seda, C., (2018). Authentic assessment: Creating a blueprint for course design. Assessment & Evaluation in Higher Education, 43(5), pp.840-854.