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It Takes a Village... Program-Wide Approaches to Redesigning Assessment in a Time of Generative Artificial Intelligence (GenAI)

◐ medium · 2026-08-20 · assessment, generative-ai, higher-ed, teacher-role, curriculum-design, faculty-development

Synthesis: Nicola-Richmond et al. (2025) present a qualitative interpretivist study — semi-structured interviews with academics (n = 12) and students (n = 17) at a large Australian university — exploring lived experiences and views of Generative AI in relation to Assessment. Thematic analysis surfaced three themes: (1) emotions, academic role and identity; (2) assessment must change; (3) the work and expertise required to change assessment. Academics and students reported both positive and negative emotions toward GenAI, and pervasive concerns about the complexity and resources needed to change assessment. In response the authors propose that redesigning assessment in the GenAI era takes a village — collaborative, program-wide approaches that bring together diverse expertise (assessment design, GenAI applications, subject matter, program knowledge, industry, and evidence base) to redevelop assessment collaboratively.

Key Findings

  • Theme 1 — Emotions, academic role and identity: GenAI evoked a range of positive and negative emotions in both groups. Some academics found GenAI's impact "hurtful" and feared the "diminishment of critical thinking skills" and "the future of my discipline," while others were excited and moved from seeing GenAI as "a threat" to a tool students must learn to use. Academics felt "behind the eight-ball," perceived their role would change (from transmitter of knowledge toward guiding the process), yet valued the continued importance of human interaction.
  • Theme 2 — Assessment must change: Both groups agreed Assessment must change to (a) assure student learning (verifying graduates achieve promised learning outcomes) and (b) prepare students for a GenAI world of work. Most opposed banning GenAI, and participants emphasized assessing differently — building GenAI use into the curriculum, designing tasks that minimize unhelpful GenAI use, and requiring critical evaluation of AI output rather than passive consumption.
  • Theme 3 — The work and expertise required to change assessment: Academics cited inflexible processes and long lead times, the complexity of redesign (especially in large-cohort, accredited courses), significant time and cost burdens, and the need for pragmatism. They strongly expected universities to provide workload capacity, training, exemplars, and tools.
  • The "village" proposal: Effective assessment redesign in the GenAI era exceeds any single academic's capability; it requires a collaborative team with expertise in assessment design, GenAI applications, subject matter, program knowledge, industry use, and the evidence base.
  • Program-wide approach: The authors advocate a program-wide (rather than individual-task or programmatic) approach to assessment redesign, allowing strategic placement of GenAI-literacy development and assurance-of-learning points across a qualification while managing resource constraints.
  • Universities' role: Institutions must make sustained investments — policy change, frameworks, professional development, and tools (portfolio/curriculum-mapping software) — to meaningfully support academics undertaking assessment change.

Study Design & Method

This is an empirical qualitative study using a qualitative interpretivist approach. Data were collected via online semi-structured individual interviews with 17 students and 12 academics across the university's four faculties (Health; Science, Engineering and Built Environment; Business and Law; Arts and Education). Interviews explored participants' understanding and use of GenAI tools, the impact on assessment practices, the role of the university, how (if at all) assessment needed to change, and the future of university assessment. Data were analyzed using Braun and Clarke's six-phase thematic analysis with inductive coding, inter- and intra-rater reliability checks, and refinement through team discussion and consensus. Ethical approval was granted by the Deakin University Human Ethics Committee (HAE-23-057). The paper focuses primarily on academic data, using student data as a supplement. Limitations include a single-institution sample, minimal demographic data (to protect anonymity), and a single point-in-time data collection amid rapid GenAI change.

Implications for AI in Education

The study provides empirical grounding for assessment reform in the GenAI era. It empirically supports Lodge et al.'s (2023) dual principles — assessment should both prepare students for a world where GenAI is ubiquitous and assure learning — while revealing the real-world friction of enacting them: systemic inertia, workload, and the need for new expertise. For educators and institutions, it shifts the framing of assessment redesign from an individual task to a collaborative, program-wide endeavor in higher education, connecting to Authentic Assessment (assessing application and critical judgement over memorization), Formative Assessment, Curriculum Design, and Faculty Development (dedicated time, training, and support to engage with GenAI). The "village" model offers a practical response to the resource and expertise gaps that make structural assessment change so difficult, echoing broader calls to treat GenAI as an opportunity to fix long-standing weaknesses in Assessment design.

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Citation

Nicola-Richmond, K., Dawson, P., Partridge, H., & Macfarlane, S. (2025). It takes a village... Program-wide approaches to redesigning assessment in a time of generative artificial intelligence (GenAI). Journal of University Teaching and Learning Practice, 22(7). (CC BY-ND 4.0.)