Track chairs

Dr Yun Chen, The University of Salford
Dr Eleni Tzouramani, University of the West of Scotland

Track Call

Information Systems (IS) education has always involved balancing different priorities: technical skills and social understanding, practical application and critical thinking, and short-term industry needs alongside long-term educational goals (Shehata et al., 2025). Over the past decade, these challenges have become even more significant. Organisations have undergone rapid digital transformation (Kulichyova et al., 2025; Shahzad et al., 2025); data and analytics have become central to management practice; platform economies, algorithmic management, and intelligent automation have reshaped work and society. For IS educators, the question is not simply how to keep curricula current, but how to equip students with the conceptual depth and critical confidence to engage with technologies they will encounter in forms that do not yet exist. This education track sits directly within the UKAIS 2027 conference theme, i.e. the role of information systems: past, present and future, recognising that IS education itself has a history worth learning from, a present demanding urgent response, and a future that must be consciously shaped.

Within this broader challenge, intelligent and generative technologies have received particular attention. From large language models that produce fluent text on demand (Kim et al., 2026; Yusuf et al., 2024), to agentic AI systems capable of autonomous planning and multi-step action (Alqurni, 2026), these tools have arrived in classrooms, assessments, and professional practice simultaneously. These developments have arrived in education already navigating a crisis of academic integrity. Recent data indicate that 88% of UK students reported using generative AI tools for assessments in 2025, up from 53% previous year (HEPI, 2025).  Three in four campus technology officers identify generative AI as a moderate to significant risk to academic integrity (Inside Higher Ed, 2026). At the same time, algorithmic and AI literacy is being recognised as a new foundational competency: a re-framing of digital literacy that promotes the ability to critically interpret, interrogate, and contest algorithmic systems and the data they consume (Archambault, 2024). Framing this solely as a governance problem misses the point: it is equally a pedagogical one and educational institutions are under pressure to respond. Questions of data governance, digital equity, platform ethics, cybersecurity literacy, information quality, and the responsible design of sociotechnical systems all demand sustained pedagogical attention alongside the AI conversation (Kangwa et al., 2025).

A thread running through these concerns is what we might call the cognitive and critical dimension of IS education: whether students develop not only technical competencies but the capacity to reason independently, evaluate evidence, and exercise judgement in the face of complexity and uncertainty (Ateeq et al., 2024). There is growing concern that delegation of cognitive tasks to AI tools (e.g. drafting, summarising, problem-solving) may gradually weaken the skills that higher education is designed to develop (Abbosh et al., 2025; Kamalov et al., 2023), which researchers have described as “cognitive atrophy” (Roxin, 2025). But this risk is not unique to AI: it applies wherever students engage with IS artefacts, from enterprise systems that pre-structure decisions to search algorithms that pre-filter information (Mueller, 2022). IS educators are particularly well placed to name and address this, drawing on a rich tradition of critical and sociotechnical scholarship.

As in previous years, a central concern of this track is responsibility: digital, corporate, and pedagogical. As educators, we have a responsibility to take a balanced view and base our teaching and decisions on evidence, distinguishing what IS and intelligent technologies can genuinely do from what is merely predicted, promoted, or feared. We also have a duty to ensure that IS education does not reinforce inequality. Our pedagogies, assessments, and curricula must support students from diverse backgrounds, with different needs and aspirations, while encouraging critical engagement with the technologies and systems we teach about rather than uncritical adoption. Our role is not simply to innovate, but to ensure that innovation supports equity and social justice.

Track areas

The track invites critical papers that explore IS and emerging technologies both as a subject that we teach about (IS management education) and as a tool that we as educators use (technology in IS education) across curriculum, classrooms, policy, and practice.

A central theme will be critical and responsible engagement with IS: helping students understand not only the capabilities and affordances of technologies, but their limitations, risks, and social consequences. Submissions are encouraged to be specific about the technologies, practices, or contexts under discussion, i.e. clarifying what they can and cannot do, in what contexts, and with what consequences. We welcome historically grounded and theoretically rich work alongside empirical and practice-facing contributions. In keeping with the conference theme, we particularly welcome papers that connect current challenges in IS education with broader questions of ethics, equity, and human agency; while also reflecting on how IS education has changed over time and where it should go next.

Examples of relevant topics include:

  • history and evolution of IS education: tracing the field’s pedagogical traditions, disciplinary identity, theoretical foundations over time, and lessons learned;
  • IS curriculum design and the challenge of keeping pace with digital transformation: balancing technical, critical, and professional formation;
  • platform economies, algorithmic management, and intelligent automation as subject matter in IS education;
  • digital workforce readiness and the evolving IS-related competencies demanded by data-driven and AI-augmented organisations;
  • generative and agentic AI in education: pedagogical design, governance, opportunity, and risk;
  • academic integrity, assessment redesign, and the pursuit of authentic learning in the age of AI;
  • algorithmic and AI literacy as foundational curriculum content and graduate capability;
  • data governance, platform ethics, and responsible sociotechnical design in the IS curriculum;
  • cybersecurity literacy and digital safety as components of IS education;
  • data literacy, information quality, and developing students’ capacity to navigate post-truth information environments;
  • critical IS education: cultivating students’ capacity to interrogate, not only deploy, digital technologies and IS artefacts;
  • IS education and the development of independent judgement, epistemic resilience, and reasoning under uncertainty;
  • corporate digital responsibility and IS ethics across curricula and professional contexts;
  • evidence-based IS teaching: what works, for whom, and under what conditions;
  • equity, diversity, and social justice in IS pedagogy, assessment, and curriculum design.

We will be accepting full papers and research in progress (RIP). Traditional research studies as well as good practice and intervention pieces are welcome, e.g. teaching interventions, best practice examples, innovations, technology adoption roadmaps, teaching cases, teaching toolkits, curriculum transformation plans or reviews, policy proposals or implementations, and regulatory gap analyses. We welcome papers based on the full spectrum of methodologies: qualitative, quantitative, or mixed methods.

All submissions should follow the conference submission guidelines. We recommend a structure explaining what the work is about, why it is needed, how it is (to be) conducted, and its (anticipated) contribution – so what – to discourses on IS education and making the world a better place. Submissions may also be nominated for potential inclusion in the UKAIS teaching cases repertoire and the teaching innovation award. Authors may use any material or medium appropriate to their work during presentation slots at the conference.

We look forward to receiving your submissions to the track by the conference deadlines.

Bibliography to help authors tap into topical conversations

Abbosh, A., Al-Anbuky, A., Xue, F., & Mahmoud, S. S. (2025). Perspective on the Role of AI in Shaping Human Cognitive Development. Information, 16(11), 1011. https://doi.org/10.3390/info16111011

Alqurni, J. (2026). Exploring the role of agentic AI in fostering self-efficacy, autonomy support, and self-learning motivation in higher education. Frontiers in AI. https://doi.org/10.3389/frai.2026.1738774

Archambault, SG (2024). Toward a new framework for teaching algorithmic literacy. Information and Learning Sciences, Vol. 125 No. 1-2 pp. 44–67, doi: https://doi.org/10.1108/ILS-07-2023-0090

Ateeq, A., Alzoraiki, M., Milhem, M., and Ateeq, R. A. (2024). Artificial intelligence in education: implications for academic integrity and the shift toward holistic assessment. Frontiers in Education, 9, 1470979. https://doi.org/10.3389/feduc.2024.1470979

HEPI (2025). HEPI Student Academic Experience Survey 2025. Higher Education Policy Institute.

Inside Higher Ed. (2026). Available at Inside Higher Ed | Higher Education News, Career Advice, Events and Jobs (accessed on 11th May, 2026)

Kamalov, F., Calonge, S., and Gurrib, I. (2023). New era of artificial intelligence in education: towards a sustainable multifaceted revolution. Sustainability, 15(16), 12451. https://doi.org/10.3390/su151612451

Kangwa, D., Msafiri, MM and Fute, A. (2025). Exploring the Factors That Promote a Balance Between Academic Integrity and the Effective Use of GenAI Tools in Higher Education: A Systematic Review. In:  Journal of Computer Assisted Learning 41, no. 5: e70109. https://doi.org/10.1111/jcal.70109.

Kim, J., Lee, SS., Detrick, R. et al. (2026). Students-Generative AI interaction patterns and its impact on academic writing. J Comput High Educ 38, 504–525 (2026). https://doi.org/10.1007/s12528-025-09444-6

Kulichyova, A., Kazantsev, N., White, L. & Islam, N. (2025) Digital transformation in large established organisations: Four restructuring dilemmas based on dynamic capabilities. International Journal of Management Reviews, 27, 420–450. https://doi.org/10.1111/ijmr.12395

Mueller, B. (2022). Corporate digital responsibility. Business & Information Systems Engineering, 64(5), 689–700. https://doi.org/10.1007/s12599-022-00760-0

Roxin, I. (2025). Generative AI: The risk of cognitive atrophy. Available at: Generative AI: the risk of cognitive atrophy – Polytechnique Insights (Accessed on 12th May).

Shahzad, K., Imran, F., & Butt, A. (2025). Digital Transformation and Changes in Organizational Structure: Empirical Evidence from Industrial Organizations. Research-Technology Management, 68(3), 25–40. https://doi.org/10.1080/08956308.2025.2465706

Shehata, B., Tlili, A., and Huang, R. (2025). Implications and challenges of technology adoption in education: a 20-year analysis of Horizon Reports. TechTrends, 69, 162–175. https://doi.org/10.1007/s11528-024-01027-z

Yusuf, A., Pervin, N. & Román-González, M. (2024).  Generative AI and the future of higher education: a threat to academic integrity or reformation? Evidence from multicultural perspectives. Int J Educ Technol High Educ 21, 21 https://doi.org/10.1186/s41239-024-00453-6