{"id":25896,"date":"2026-04-29T11:16:27","date_gmt":"2026-04-29T01:16:27","guid":{"rendered":"https:\/\/educational-innovation.sydney.edu.au\/teaching@sydney\/?p=25896"},"modified":"2026-04-29T12:07:04","modified_gmt":"2026-04-29T02:07:04","slug":"why-feedback-matters-and-matters-more-for-open-assessments","status":"publish","type":"post","link":"https:\/\/educational-innovation.sydney.edu.au\/teaching@sydney\/why-feedback-matters-and-matters-more-for-open-assessments\/","title":{"rendered":"Why feedback matters (and matters more) for open assessments"},"content":{"rendered":"

The ability and availability of generative AI tools — which can complete many of our assessments, including quizzes, assignments, reports, reflections and presentations, potentially with minimal student thinking or understanding — means that these assessments can no longer be treated as reliable evidence<\/strong> <\/em>of students\u2019 capabilities when completed in unsupervised environments. Completion of these tasks and products are, though, still valuable as ways to develop<\/strong> <\/em>students\u2019 thinking and capabilities, especially when supported by the use of contemporary technologies as they are used in the discipline. For this to happen, students also need feedback to guide them and help them improve. Increasingly, this will include feedback on the quality of their judgements, choices, and thoughtful use of tools such as generative AI alongside traditional academic skills.<\/p>\n

The distinction between secure assessments (Lane 1)<\/a>, which provide trustworthy assurance of<\/em> <\/strong>learning, and open assessments (Lane 2)<\/a>, which are designed primarily for<\/em> <\/strong>learning, is a clarification of purpose and ensures we can maintain and improve standards. As a result, this categorisation fundamentally reshapes how we should approach and think about marking and its purpose.<\/strong> The shift does not ask markers of open assessment to make weaker judgements, but different ones, because the assurance function has been deliberately relocated to secure assessments elsewhere in the program.<\/p>\n

The two lane approach<\/a> is not an interim response to AI. It recognises that we can only reliably control assessment conditions in supervised environments, and that outside those settings we must design for learning rather than the policing of how AI has been used.<\/p>\n

The assessment product is no longer a reliable proxy<\/h2>\n

This article builds on a recent Needed Now in Learning and Teaching<\/em> post, What are we going to do about marking now?<\/a>, which included the statement \u201cthe artefact is no longer a reliable witness\u201d<\/em>. The vast majority (~75%) of our assessments at Sydney are categorised as open, as they should be. Just under half (~48%) of all marks are given to assessments in the open \u201cproduction and creation\u201d category \u2013 written work, presentations, portfolio, journals, creative work and performance. It is pleasing and clear that we value these open assessment types.<\/strong> They also play an important role in developing many of the skills valued by employers, including creativity, critical thinking, and reflection. However, those employers are increasingly expecting our graduates to work effectively with AI tools to generate options, interpret and critique perspectives, iterate purpose and content, and produce polished content quickly. To help our students develop these capabilities, we need to concentrate on the processes they use, including the choice and ways they use tools, not just on the final product.<\/p>\n

We\u2019ve long known that we never assess learning directly. We make professional judgements sometimes on direct observation but often based on proxies, drawing on available evidence of understanding and quality (Sadler, 2013<\/a>). As Boud and colleagues note (2018<\/a>), we often rely on contextual signals that stand in<\/em> for learning rather than observing learning itself. What AI has done is expose how fragile those inferences can be when persuasive features such as fluency, structure, and disciplinary language are easy to generate on demand without the student necessarily exercising disciplinary judgement. The \u201c4P framework\u201d (Fawns et al. 2026<\/a>) focusses on product, process, performance, and practice in the design of balanced assessment.<\/p>\n

The risk is turning marking into an exercise in AI detection and suspicion. The two lane model deliberately avoids this trap. As AI tools are very good at producing polished content, marking needs to focus on how well ideas are connected and claims are justified, rather than on surface features of production. In open assessments, markers are not being asked to reconstruct how learning occurred. Their task is to judge the quality of reasoning, decisions, and judgement evident in the work itself and, importantly, to help students see how to improve. It is not their task to infer that a student has or has not learned based on how fluent, generic, or \u2018AI\u2011sounding\u2019 a submission appears.<\/p>\n

Open assessments as assessment for learning<\/h2>\n

Under the Sydney Assessment Framework, open (lane 2) assessments are not designed to independently assure learning outcomes. That responsibility sits with secure (lane 1) assessments positioned at key points across the program. This frees us to concentrate completely on learning and development when we design and mark open assessments. These exist to:<\/p>\n