Call for Papers for Special Issue on Evidence of Learning in the Age of Generative AI: From Product to Process

13-08-2026

Under many traditional assessment practices, the submitted artefact (an essay, report, or exam script) has served as the primary demonstration of specified learning outcomes. Whether assessment is summative or formative, the quality of a student’s learning is not observed directly but inferred from the artefact they produce (Sadler, 1989). However, Generative AI (GenAI) has challenged this model. When a competent-looking artefact can be produced without the cognitive effort of deep learning, the product can no longer be trusted as a warrant for the learning behind it. Because detecting GenAI use with certainty is challenging, investing primarily in detection is inadequate; only structural assessment changes will suffice (Corbin et al., 2025; Lodge et al., 2025).

To form trustworthy judgements about student learning (Lodge et al., 2023), two promising bodies of work point beyond securing the written product. First, as highlighted in recent TEQSA guidance (Lodge et al., 2023; Lodge et al., 2025), institutions must shift to the process of learning by capturing multiple points of evidence. Demonstrations of student capability over time, including drafts, revisions, dialogue, and secure formats such as oral presentations and supervised demonstrations, can carry evidentiary weight by allowing students to articulate their reasoning and defend claims in real time. However, capturing the process is not sufficient on its own. A growing body of process-analytic work is beginning to characterise how students and GenAI interact during a task, offering early evidence that these exchanges vary considerably in depth, ranging from quick delegation to more sustained, evaluative dialogue (López-Pernas et al., 2025; Rinja et al., 2026). This variability is part of what makes process data valuable, since it can reveal forms of cognitive offloading that a finished artefact would otherwise conceal. At the same time, it points to an open question the field is still working through: what features of an interaction trace would allow it to be read as trustworthy evidence of learning. Second, scholarship on evaluative judgement (Tai et al., 2018) reframes assessable human capability as the capacity to recognise quality, verify claims, and decide whether to delegate tasks to AI. TEQSA’s most recent guidance integrates these strands, naming distributed cognition and hybrid metacognition as adaptive capabilities for a GenAI-integrated future (Lodge et al., 2026). Yet richer evidence has its own cost: capturing process can shade into surveillance, and raises questions of privacy and equity that sit alongside, not beneath, questions of validity.

Aim of the Special Issue

This special issue invites short, rigorous papers that reconceptualise evidence of learning for a world in which GenAI can produce the artefact. We seek work that treats the process of learning and the judgement exercised within it, rather than the finished product, as the warrant for learning.

Much about these approaches remains unresolved. While capturing the process of learning and assessing evaluative judgement offer promising alternatives to artefact-based grading, little is known about the feasibility, integrity, and validity of these process-oriented and dialogic methods in digital and blended environments. Furthermore, critical constructs for higher education in an environment where generative AI is ubiquitous, such as knowing what good looks like, defending one's process, and verifying AI outputs at scale, remain weakly operationalised. Finally, while program-wide assessment reform is recommended to assure learning across degree structures, practical frameworks for scaling these process-driven methods across diverse cohorts are markedly underexamined.

We welcome two kinds of contribution: work that designs and enacts an assessment approach itself — such as an interactive oral assessment — and reports on its use; and work that analyses evidence from existing or past assessments to understand the process of learning. Conceptual pieces spanning both are equally welcome.

Suggested Topics of Interest

Authors may use the following suggested topic areas as guidance, but we welcome author-initiated topics that are relevant to the aim of the special issue.

  • Dialogic and oral forms of assessment (interactive orals, vivas, supervised demonstrations) as secure, GenAI-resilient approaches through which students articulate reasoning and defend their work.
  • Programmatic and process-oriented assessment designs that assemble multiple points of evidence across tasks and over time to assure learning at the program level.
  • The integrity and security of process- and judgement-based assessment in digital and blended delivery.
  • Capturing the process of learning (drafts, revisions, trace data) as multiple points of evidence, and the analytic methods (learning analytics, process mining, network analysis) that make it assessable and establish its validity and reliability.
  • Assessing evaluative judgement: students' capacity to recognise quality, verify and challenge AI output, and decide when and when not to delegate to AI.
  • Documenting learning authentically without surveillance, including privacy, consent, and student agency.
  • Fairness and inclusion in process- and judgement-based evidence across diverse learners.
  • Developmental trajectories of process documentation and evaluative judgement across K-12 stages and into tertiary study.
Educational Contexts

We welcome submissions set in diverse educational backgrounds, including:

  • K-12 education
  • Higher and further education
  • Faculty professional development
  • Vocational education and training
  • Teacher education and development
  • Educational policy, spanning institutional to national spectrums
  • Development and implementation of educational technology
Submission Guidelines Types of articles accepted
  • Original and developing qualitative, quantitative and mixed-methods research
  • Rapid reviews
  • Theoretical/conceptual pieces
Format, length, style

Articles are limited to 2,500 words in order to present succinct research. Supplementary material can be lodged in electronic format, including publication of data, scripts, and additional methodological procedures. Larger datasets must be deposited in recognised public domain databases by the author.

The 2,500 word limit is for the manuscript body. The abstract and references are not included in the word count. ALL other text is included. The word count is strictly enforced. Please keep figures and tables to a minimum.

Full submission guidelines may be found here: https://learningletters.org/index.php/learn/about/submissions

Key dates & deadlines
  • 9 November 2026: Submission of title, keywords and 200-250-word abstract.
  • 22 November 2026: Notifications of abstract acceptance by this date.
  • 1 February 2027: Full paper submission deadline.
  • 1 March 2027: Notifications of full paper acceptance by this date.
  • 22 March 2027: Final versions of revised paper due.
  • May 2027: Publication of special issue.
Where and how to submit

For questions regarding the special issue, please contact the guest editors.

Key References

Boud, D., & Dochy, F. (2010). Assessment 2020: Seven propositions for assessment reform in higher education. Australian Learning and Teaching Council.

Corbin, T., Dawson, P., & Liu, D. (2025). Talk is cheap: why structural assessment changes are needed for a time of Gen AI. Assessment & Evaluation in Higher Education (online first).

Lodge, J. M., Bearman, M., Dawson, P., Gniel, H., Harper, R., Liu, D., McLean, J., Ucnik, L. & Associates (2025). Enacting assessment reform in a time of artificial intelligence. Tertiary Education Quality and Standards Agency, Australian Government.

Lodge, J. M., Howard, S., Bearman, M., & Dawson, P. (2023). Assessment reform for the age of artificial intelligence. Tertiary Education Quality and Standards Agency.

Lodge, J. M., de Barba, P., Ainscough, L., Brazil, J. R., Broadbent, J., Ebbert, D., Frankland, S., Gabriel, F., Gašević, D., Hennicke, T., Lim, L.-A., Male, S. A., Mirriahi, N., Oliveira, E. A., Pacitti, H., Raković, M., Russell, J., Taylor-Griffiths, D., & Yang, S. (2026). Assuring quality learning in a gen AI-integrated future: The role of adaptive capabilities. Tertiary Education Quality and Standards Agency, Australian Government.

López-Pernas, S., Misiejuk, K., Oliveira, E., & Saqr, M. (2025, November). The dynamics of the self-regulation process in student-AI interactions: The case of problem-solving in programming education. In Proceedings of the 25th Koli Calling International Conference on Computing Education Research (pp. 1-12).

Rinja, D., Oliveira, E.A., López-Pernas, S., Saqr, M., Specht, M., Misiejuk, K. (2027). Unpacking Vibe Coding: Help-Seeking Processes in Student-AI Interactions While Programming. In: Blanchard, E.G., Chen, G., Chi, M., Isotani, S. (eds) Artificial Intelligence in Education. AIED 2026. Lecture Notes in Computer Science(), vol 16584. Springer, Cham. https://doi.org/10.1007/978-3-032-29763-1_35

Sadler, D. R. (1989). Formative assessment and the design of instructional systems. Instructional Science, 18(2), 119–144.

Tai, J., Ajjawi, R., Boud, D., Dawson, P., & Panadero, E. (2018). Developing evaluative judgement: Enabling students to make decisions about the quality of work. Higher Education, 76(3), 467–481.