Maintaining a focus on sustainable and authentic assessment in this era of generative AI

Generative artificial intelligence (gen AI) is highlighting issues that those of us involved with the Faculty of Science Assessment Working Group and many others across campus talked about at the start of the pandemic.

Meaningful. Authentic. Developmental. Rich in feedback. Opportunities for reflection. Supportive of student well-being. Foster a culture of academic integrity. Clear expectations. Transparency. Assessments that are accessible, equitable, inclusive, and fair.

Current concerns revolving around assessment of student learning in this era of gen AI overlap significantly with matters associated with academic integrity and weaknesses with some traditional assessment tasks means that these tasks might not be robust enough. We need to take a close look at what we are teaching, the learning we desire from our students and the assessments we are using and pose a couple of key questions. Is this still relevant, is it feasible to assess and, if so, what are worthwhile ways to assess this learning? Integrating what we already know about effective assessment practice has become increasingly important as use of Gen AI tools becomes commonplace.

We have a solid foundation of research into effective assessment practices to work from. Research by Boud (2000), Boud and Associates (2010), Eaton and Turner, (2020), Gibbs, (2006), Jones and colleagues (2021), Tai and colleagues (2023), and Zawacki-Richter and colleagues (2019) challenges us to incorporate elements of wellbeing, EDI, social justice, accessibility, and ethics into our assessments along with emerging technologies like gen AI.

A recently published document offers some guidance we might consider as we think about how, what and when we assess students’ learning (Lodge et al., 2023). Developed by Australian scholars in artificial intelligence, assessment, and higher education these guiding principles and supporting propositions can be applied to all course delivery modes, although this will likely present different challenges and opportunities depending on the format and inform our decision-making around assessment in ways that reflects the influences and impacts of artificial intelligence.

Guiding principles and propositions

1. Assessment and learning experiences equip students to participate ethically and actively in a society where AI is ubiquitous.
2. Forming trustworthy judgements about student learning in a time of AI requires multiple, inclusive and contextualized approaches to assessment.

The document continues with these five aspirational propositions and offers helpful elaborations and examples for each.

Assessment should emphasize…

1. …appropriate, authentic engagement with AI.
2. …a systemic approach to program assessment aligned with disciplines/ qualifications.
3. …the process of learning.
4. …opportunities for students to work appropriately with each other and AI3. …the process of learning.
5. …security at meaningful points across a program to inform decisions about progression and completion.

Changes to our assessment practices at this scale promise all sorts of complexity, especially when we figure in requirements for accreditation, certification as well as the workload involved. The team members in the Teaching and Learning Centre are ready to support instructors, departments and Faculties in learning about AI for teaching and learning, re-designing learning activities and assessments for courses, and working with curriculum committees as they revisit assessment processes at the program level.

References

Boud, D. (2000). Sustainable assessment: rethinking assessment for the learning society. Studies in Continuing Education, 22, 2, 151–167. https://doi.org/10.1080/713695728

Boud, D. and Associates (2010). Assessment 2020: Seven propositions for assessment reform in higher education. Australian Learning and Teaching Council. https://www.uts.edu.au/sites/default/files/Assessment-2020_propositions_final.pdf

Eaton, S. E., & Turner, K. L. (2020). Exploring academic integrity and mental health during COVID-19: Rapid review. Journal of Contemporary Education Theory & Research (JCETR), 4(2), 35–41. https://files.eric.ed.gov/fulltext/ED608658.pdf

Gibbs, G. (2006). How assessment frames student learning. In Innovative assessment in higher education (pp. 43–56). Routledge. https://www.taylorfrancis.com/chapters/edit/10.4324/9780203969670-13/assessment-frames-student-learning-graham-gibbs

Jones, E., Priestley, M., Brewster, L., Wilbraham, S. J., Hughes, G., & Spanner, L. (2021). Student wellbeing and assessment in higher education: the balancing act. Assessment & Evaluation in Higher Education, 46(3), 438–450. https://doi.org/10.1080/02602938.2020.1782344

Lodge, J. M., Howard, S., Bearman, M., Dawson, P, & Associates (2023). Assessment reform for the age of Artificial Intelligence. Tertiary Education Quality and Standards Agency. https://www.teqsa.gov.au/sites/default/files/2023-09/assessment-reform-age-artificial-intelligence-discussion-paper.pdf

Tai, J., Ajjawi, R., Boud, D., Jorre de St. Jorre, T. (2023) Promoting equity and social justice through assessment for inclusion. In pp 9–18. Ajjawi et al. (Eds). Assessment for Inclusion in Higher Education: Promoting Equity and Social Justice in Assessment. Routledge, Taylor & Francis Group. https://www.taylorfrancis.com/books/oa-edit/10.4324/9781003293101/assessment-inclusion-higher-education-rola-ajjawi-joanna-tai-david-boud-trina-jorre-de-st-jorre

Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education–where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 1–27. https://doi.org/10.1186/s41239-019-0171-0