Assessment & Wellbeing

Knowledge Mobilization

Please email Lia at lia.daniels@ualberta.ca for more information on the outcomes of this SSHRC funded project.

Please note that many of the images displayed here were made with Gemini or related google based AI platforms. All content has been reviewed by Daniels for accuracy. No writing was produced by GenAI nor was the layout or choices about what content to present.

The Research Problem

Well-being and assessment may seem to be an unexpected pairing. However, as the mental health crisis on Canadian campuses grows, supports for student well-being have become a standard of care in post- secondary education and assessment practices should not be exempt. By explicitly linking assessment to student well-being through a motivation framework, this program of research leveraged a contemporary and pressing reason to change assessment practices in higher education. Motivation theory is well poised for this task with robust theories and decades of research showing that when students are intrinsically motivated, they experience less anxiety, are more creative, persist in the face of challenge, and reach ambitious goals. Unfortunately, students are overwhelmingly extrinsically motivated by assessment. While extrinsic motivators are highly effective for compliance, they come with costs including increased stress, anxiety, procrastination, and academic dishonesty – the exact sort of things that indicate poor well-being. In this program of research, we reoriented assessment practices to prioritize student well-being by creating and testing the effectiveness of intrinsically motivating assessments.

The Research Pipeline

This program of research created opportunities to partner with post-secondary instructors to revision assessment practices for the benefit of student well-being. A the program of research unfolded a few priorities took root:

Instructors retained authority over all assessment decisions and were the content expert. This meant we worked with instructors, their beliefs, and institutional tensions throughout the project. There were many systemic realities that prevented changes to assessment practices.
We wanted to collect a variety of forms of evidence including pure qualitative and descriptive information on student well-being and assessment as well as true experimental data to test causal mechanisms.
Our workflow needed to provide instructors with tangible solutions as well as explanations for why we believed certain changes would benefit stuent well-being.
Instructor well-being emerged as an important consideration alongside student well-being.

Introduction to the Project

Below are several resources we created for participants in the project to provide a foundation in self-determination theory and our conceptualization of student wellbeing. We also encourage people to read the foundational chapters associated with this program of research.

imap welcome 1Video made with Biteable. The World's Simplest Video Maker

Article Link

Descriptive Foundations

Early output from this program of research focused on describing and understanding connections between assessment and well-being in its current state. In addition to general student perspectives, we were interested in understanding specific groups of students including student athletes, Black students, and students with dyslexia. Below, we provide infographics created by NotebookLM for each discrete study and the reference to the published manuscript. A two take aways from the series of studies are (1) confirming the extant literature, assessment is a source of ill-being for students (2) there is no specific format of assessment that students automatically and unanimously recognize as beneficial for their wellbeing.

Parker, PC., Goegan, LD., & Daniels, LM. (2024). An exploratory mixed-method study on student-athletes’ motivation for assessment in sport and academic settings. Cogent Education, 11:1, 2298613.

Daniels, LM., Ferede, S., & Scott-Ugwuegbula, Z. (2025). Black university students’ lived connections between classroom assessment and motivation. Contemporary Educational Psychology.

Daniels, L.M., Goegan, LD., & Parker, PC. (2023). The role of fairness and basic psychological needs in understanding dyslexic students’ emotions in classroom assessment. Learning Disabilities: A Contemporary Journal, 21(2), 159-176.

Daniels, LM. & Wells, K. (2025). Connecting students’ descriptions of classroom assessment in higher education with wellness. Assessment and Evaluation in Higher Education, 50, 366-380.

Global Recommendations Document (GRD)

To communicate with instructors we created what became known as the "Global Recommendation Document" - or GRD. The GRD contained an evaluation of all aspects of an instructor's course that pertained to classroom assessment. One of the most important revelations from this process was the need to start the assessment evaluation with the learner outcomes of the course. In retrospect, given that all assessment design recommendations suggest connecting assessments to learner outcomes, we should have anticipated this starting point. We decided to use a form of an assessment blueprint to map learner outcomes to the full assessment plan. This allowed us to identify overlap and gaps between the learner outcomes and the full course assessment plan. This also served as a valuable visualization for instructors! If some outcomes were over-represented, we suggested that they may not all be necessary. If other outcomes were missing, we suggested that some form of assessment was necessary for evidence of that learning (or that the outcome was not actually pertinent to the course). This also supported conversations about the weight of assessment. Our research has shown that students' well-being really changes when assessments are valued at more than 30% or less than 5%. The former increases stress by having so much on one assessment and the latter feels like it isn't really worth the time. To mimic the process we undertook in creating the GRD, we have designed an Assessment Architect App that you can use to see how assessment decisions relate to need satisfaction and frustration - as well as your own workload. Click through the options on the left and see how it changes well-being scores on the right. Please note the percentages are descriptive only!

This App was created by Dr. Daniels with Gemini App features using vibe coding processes.

Need Supportive Features for Assessment

We drew on two perspectives from Self-determination Theory to discern what sort of revisions could be applied to discrete forms of assessment to produce theoretically consistent improvements in well-being via basic psychological needs.

Source #1

Reeve, J. (2016). Autonomy-supportive teaching: What it is, how to do it. In Building autonomous learners: Perspectives from research and practice using self-determination theory (pp. 129-152). Singapore: Springer Singapore.

In this chapter, Reeve articulates how six instructional practices form the basis of the autonomy-supportive intervention that has documented gains for instructors and students. The six practices are illustrated below with the supportive action on the top of the image and the frustrating action on the bottom.

Source #2

Ahmadi, A., et al. (2023). A classification system for teachers’ motivational behaviors recommended in self-determination theory interventions. Journal of Educational Psychology, 115(8), 1158–1176. https://doi.org/10.1037/edu0000783

In this delphi study, Ahmadi and colleagues undertake an iterative concensus process to identify specific practices that embody the theoretical principles of autonomy, compentence, and relatedness satisfaction and frustration. The image below shows the shortlist of 22 emblematic behaviours that support or thwart BPN.

Drawing across these resources, we translated these instructional practices into features that could be added to assessments. We focused on three categories: Features for multiple-choice tests; Features for constructed response assessments; and Features for course-wide assessment decisions. These features are described in detail in the main two empirical manuscripts documenting the effectiveness of the features and summarized in the image below which is a summary of Table 1 from Daniels, Wells et al. (2026).

Daniels, LM., Wells, K., Lindner, M., Beeby, A., & Daniels, VJ. (2026). Satisfaction and frustration of basic psychological needs in classroom assessment. Trends in Higher Education, 5(1), 15. doi: 10.3390/higheredu5010015. This paper contain three studies: a validation, a randomized experiment, and an embedded classroom test of the intervention all applied to multiple-choice tests. In all studies, the addition of need supportive features improved indicators of student well-being above and beyond the effects of simply improving the quality of multiple choice items themselves.

Daniels, LM., Wells, K., West, CV., Becker, A., & Poth, CN. (2026). A multi-method case study of student and instructor well-being in assessment. Learning in Context. This paper is a collaborative case study that involves not only quantitative measures of how indicators of students' well-being changed, but also the instructor's lived experience of the changes on her own well-being.

Qualitative Instructor Results

Quantitative Student Results

UAlberta Instructors: Get a Canvas template to add need supportive features to your quizzes!

Go to Canvas. Look for the "Commons" button on the left hand side. Search for wellbeing or look for the iMAP logo.

The AI Elephant in the Room

We submitted this SSHRC grant in Fall 2021 and were notified of its results in Spring 2022. On November 30, 2022 OpenAI launched it as a free public preview of the GPT-3.5 architecture. ChatGPT reached 1 million users in just 5 days. Currently, model GPT5.6 processes more than 2.5 billion prompts every day.

Based on our conversations with instructors, ot seems like Winter 2026 was the term that instructors really started to notice GenAI in their students assessment. I don't think it is hyperbolic to state that some folks are experiencing an existential crisis about what this means for their assessment practices. As such we conclude this KM with our top three thoughts about assessment and student wellbeing in the time of GenAI.

#1 Stay focused on validity.

We don't think the results of this program of research - either the empirical evidence or the theoretical propositions that link well-being and validity - are undone by GenAI. In part, the reason people may be freaking out so much is exactly because of validity. The fundamental question instructors are asking about assessment is: Does the inference I want to make still hold? Does the work here appropriately measure what the student knows? We think some of the best thinking in this regard is being done by the team at Deakin University's Centre for Research in Assessment and Digital Learning (CRADLE). Here are some great articles to consider:

Dawson, P., Bearman, M., Dollinger, M., & Boud, D. (2024). Validity matters more than cheating. Assessment & Evaluation in Higher Education, 49(7), 1005-1016.
Corbin, T., Dawson, P., Nicola-Richmond, K., & Partridge, H. (2025). ‘Where’s the line? It’s an absurd line’: towards a framework for acceptable uses of AI in assessment. Assessment & Evaluation in Higher Education, 50(5), 705-717.
Corbin, T., Dawson, P., & Liu, D. (2025). Talk is cheap: Why structural assessment changes are needed for a time of GenAI. Assessment & Evaluation in Higher Education, 50(7), 1087-1097.
Nieminen, J. H., Bearman, M., Boud, D., Corbin, T., Dawson, P., Tai, J., & Walton, J. (2026). Grading in an age of assessment reform: the elephant in the room. Assessment & Evaluation in Higher Education, 1-17.
Corbin, T., Bearman, M., Boud, D., & Dawson, P. (2026). The wicked problem of AI and assessment. Assessment & evaluation in higher education, 51(4), 736-752.

#2 Think about the problem from many angles.

If you have done a quick search for "AI and assessment" you have likely found the AI Assessment Scale (AIAS) by Perkins and colleagues. If not, we'll save you the trouble: https://aiassessmentscale.com/ This scale is everywhere. Kudos to Perkins and colleagues for providing a descriptive framework at a time when it was much needed and for their diligence in open-access and translation work. The scale describes different levels of possible AI integration with assessment and is beginning to be paired with the type of ideas coming out of CRADLE about structural changes to assessment being required. As we start thinking about GenAI and assessment, however, we find ourselves doing the same thing we did for well-being - that is, asking what existing frameworks or perspectives could help guide our thinking. Important questions could be:

Have you thought about GenAI use developmentally? The UNESCO AI Competency Framework for students has four dimensions: human centred mindset, ethics of AI, AI skills, and AI system design. Each dimension progresses through three levels of competence modelled after Bloom starting with understanding, then applying, and finally creating. In connection to assessment, just as curricular content increases in cognitive complexity, so too should considerations about GenAI. If you choose to add GenAI literacy itself as a learner outcome, then remember to collect evidence on how well students have done that learning in addition to their usual curricular content.
Have you thought about philosophical or ethical frameworks of GenAI? Notre Dame has created an AI framework focused on formation in an AI-saturated world. The DELTA framework stands for dignity, embodiment, love, transcedence, and agency. From an assessment perspective, this framework helps us remember that sneaky approaches to monitoring, controlling, or punishing GenAI use are unlikely to the be the best way forward for assessment practices that still care about wellbeing.
Have you thought about the motivations behind when, why, and how students choose to use GenAI? As was the case in this research, SDT provides a viable framework for considering the motivational aspect of GenAI in assessment. However, we think that another theory may be more beneficial in guiding research in this area: situated expectancy value theory (SEVT)....and we have current grant proposals to explore this perspective fully.

#3 It's complicated so just try one new thing. Evaluate. Iterate.

Because GenAI is often more than sufficient at completing the assessment tasks we ask students to do, we have to think proactively about options to deal with this reality. A combination of AI policies and structural changes to assessment seem warranted. In some ways this makes our results more relevant than ever because one of the most obvious structural changes is to have students complete assessments in person! Imagine if every instructor who decided the structural solution was to have students complete in person assessments added need supportive features to those assessments! Our results may help support wellbeing even as students find themselves in more restrictive assessment settings. Other ideas still aligned with need supportive teaching include:

Run your constructed assessments and rubrics through multiple GenAI platforms to see how well it completes the task. Being informed is half of the battle.
Reduce the weight of work completed at home - or make that work fully formative! Although grades have historically been used to compel students to do that sort of work, now that GenAI can take weekly quizzes or write reflections, the decision to complete the work really depends on if students see it as a meaningful aspect of their learning. Plus, students who do decide to do it will be happier that their peers aren't getting free grades for using GenAI.
Add oral or questionning portions to your written work - and formally assess this component! Studies already show that students who use GenAI can't recall their output as well as students who do not use GenAI. Capitalize on this by grading the ability to answer meaningful questions. If you add an oral component but the scoring of it is small or non-existant then the GenAI work still is over rewarded.
Think about ways to detect "signal" of GenAI in student work and revise your scoring accordingly. Again, this is mainly a validity argument not one of policing. It doesn't really matter if a student gives a shallow response because it was formulated by GenAI or it was their own shallow idea so long as the shallow idea doesn't earn a high score on your rubric! Go back to the course learning outcomes and try to use those as the basis of a rubric that GenAI may struggle with. It is likely time to stop scoring how pretty slides are or how accurate the punctuation is - those are rarely learner outcomes anyway.

Below are slides to two sessions Dr. Daniels has co-facilitated with Dr. Demmans Epp through the Centre for Teaching and Learning in their roles as CTL Scholars. UAlberta instructors, visit the CTL website for more resources.

Rubrics Shared July 22 2026

MCQ June 17 2026 Slide Deck to share

Scholarly Output

Daniels, LM., Wells, K., West, CV., Becker, A., & Poth, CN. (2026). A multi-method case study of student and instructor well-being in assessment. Learning in Context.

Daniels, LM., Shukalek, A., & Wells, K. (under review). Uptake of Recommendations for Assessments Designed to Support Student Well-being: Instructor identified opportunities and barriers.

Daniels. LM., Daniels, VJ., Firoozi, T., & Gierl, M. (under review). Multiple-choice item writing guidelines for classroom assessment: A state-of-the-art review and use-inspired reframing.

Firoozi, T., Daniels, L., Daniels, V., & Gierl, M. (2025, July). An Augmented Intelligence System for Automated Quality Control and Feedback Generation of Multiple Choice Test Items. In International Conference on Artificial Intelligence in Education (pp. 86-93). Cham: Springer Nature Switzerland.

Daniels, LM. & Wells, K. (in production). Self-Determination Theory as a Framework for Student Assessment Well-being. Handbook of Equity in Assessment.

Daniels, LM., Ferede, S., & Scott-Ugwuegbula, Z. (2025). Black university students’ lived connections between classroom assessment and motivation. Contemporary Educational Psychology, 80, 102347.

Daniels, LM. & Wells, K. (2025). Connecting students’ descriptions of classroom assessment in higher education with wellness. Assessment and Evaluation in Higher Education, 3, 366-380.

Daniels, LM. (2024). Walking the Assessment Well-being Talk. Summit on Equity, Assessment and Evaluation in Education: Bringing Theory and Practice Together. https://doi.org/10.7939/r3-fsaj-er20

Goegan, L. D., Parker, P. C., & Daniels, L. M. (2023). Connecting Basic Psychological Needs and Assessment: Perspectives of Postsecondary Students with Dyslexia. Journal of Postsecondary Education & Disability, 36(3).

Daniels, L. M., Goegan, L. D., & Parker, P. C. (2023). The Role of Fairness and Basic Psychological Needs in Understanding Dyslexic Students’ Emotions in Classroom Assessment. Learning Disabilities: A Contemporary Journal, 21(2).

Daniels, L. M., Pelletier, G., Radil, A. I., & Goegan, L. D. (2021). Motivating assessment: How to leverage summative assessments for the good of intrinsic motivation. In Nichols, SL & Varier, D. (Eds). Teaching on Assessment. Information Age Publishing.

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