I\u2019ve been teaching engineering courses for quite a while now, and it goes without saying that generative AI (Gen AI) tools have had a substantial impact on tertiary education both at the philosophical and practical levels. As a lecturer, these tools have helped me speed up philosophically desirable but practically time-consuming tasks, such as polishing language in my student feedback or summarising responses from quick engagement activities. I also use Gen AI often to help me come up with stories and examples \u2013 I find that stories and hypothetical case studies help with engagement, so I use Gen AI to tailor these narratives to my course material.<\/p>\n
Gen AI doesn\u2019t just benefit educators; it\u2019s increasingly vital for students as well. In today\u2019s professional landscape, many industries are seeking graduates who know how to leverage AI to enhance output and efficiency in their deliverables. That\u2019s why I encourage my students to integrate Gen AI tools into their coursework (It\u2019s also a way to stress-test my assessments! One truly learns from experience)<\/em>. In my classes, they\u2019re free to experiment using Gen AI with their assignments: some might generate preliminary solutions to design problems, while others might use it as a preliminary brainstorming tool.<\/p>\n But like any new technology, Gen AI must be approached thoughtfully and ethically. Students need to understand that using an AI tool doesn\u2019t absolve them of responsibility – especially in fields of professional practice such as engineering.<\/strong> In fact, it demands even more critical thinking and diligence, where ultimately, the buck stops with the engineers if a bridge collapses or an implant fails.<\/p>\n To help guide my students, I developed the \u201c6R\u2019s of GenAI,\u201d which keep them focused on using AI as a learning partner<\/strong> rather than a crutch.<\/p>\n <\/p>\n These principles keep students mindful that generative AI is a tool \u2013 one with powerful potential to streamline tasks and spark creativity, but not a substitute for genuine understanding. It\u2019s interesting to see some submissions from students who don\u2019t follow this and simply copy-paste from Gen AI \u2013 after you use these tools for a while it actually becomes fairly obvious when a student has actually used AI simply as a content generation tool, rather than a tool that supplements and supports their understanding and application of knowledge.<\/p>\n Of course, the 6R\u2019s aren\u2019t just for my students; I apply them in my own teaching practice<\/strong> too. When I generate new case studies to illustrate a technical point or help define themes and key ideas, I always verify the details to ensure they align with the course topic and level of difficulty. If something seems off, I go back, refine my query, and make sure the results add real value to the learning materials.<\/p>\n One example of using Gen AI in teaching is for my large biomaterials unit, where students convey their understanding around materials used in industry through an oral viva assessment. I use Gen AI tools to help draft individualised feedback for nearly 200 students based on quickly written notes by the three graders in the group viva session collected through SRES. I begin by inputting raw feedback notes from myself and the tutors, as well as the rubric criteria for the grade band the student has received into combined prompts. This then generates a tailored, coherent feedback using AI. I read<\/strong> and review<\/strong> each response to ensure it addresses the student\u2019s work appropriately and aligns with the published rubrics and my recollection of the viva session. For the research <\/strong>step, I double-check the AI response with the raw grader\u2019s notes and check that the generated text does not say anything factually inaccurate, especially around any technical commentary the graders have provided. Rather than pasting the output directly, I reconcile<\/strong> it with my own understanding of the student\u2019s performance and the personal language tone I want to convey. If the draft lacks clarity, lacks depth on particular criteria, or deviates from the desired response style, I reprompt<\/strong> the AI with more specific details. Ultimately, I remain responsible<\/strong> for the feedback students receive, and I do the above for each<\/em> student and appreciate that the words will be ultimately attributed to me. Whilst inputting prompts for each student is quite repetitive, a whole lot of Ctrl C+V, and time-consuming, the efficient generation of individualised cohesive feedback<\/strong> itself interlaced with positive undertones and suggestions of helpful feedforward points for an interactive viva assessment is an enormous value-add made possible by Gen AI<\/strong>.<\/p>\n The 6Rs can be adapted across disciplines, not just in engineering. Whether you’re teaching literature, business, health sciences, or any other field, the 6R’s provide a structure for ethical and effective AI integration.<\/p>\n For educators hesitant about AI in education, I recommend starting small: use Gen AI yourself to generate ideas for activities or to refine your teaching materials. I encourage my peers to use Gen AI as much as possible themselves – one of the most effective ways to understand what the tool can do is to use it yourself!<\/p>\n To implement a similar approach in your classroom:<\/p>\n By following the 6R\u2019s, we can help students embrace Gen AI\u2019s transformative impact on teaching and learning \u2013 fostering creativity, efficiency, and a sense of ownership over one\u2019s work. AI is here to stay, and our job as educators is to show students how to harness it responsibly, and responsible Gen AI use will continue to shape the future of engineering education, and every academic field where innovation and integrity go hand in hand.<\/p>\n","protected":false},"excerpt":{"rendered":" I\u2019ve been teaching engineering courses for quite a while now, and it goes without saying that generative AI (Gen AI) tools have had a…<\/p>\n","protected":false},"author":4332,"featured_media":23046,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1368,41,9,1860],"tags":[2502,2300,110,2503,52],"coauthors":[3466],"class_list":["post-23045","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-assessment","category-engineering","category-faculty","category-offer-meaningful-assessment-feedback-and-academic-integrity-b","tag-ai","tag-artificial-intelligence","tag-assessment","tag-chatgpt","tag-teaching-insights","post-item","post-even"],"_links":{"self":[{"href":"https:\/\/educational-innovation.sydney.edu.au\/teaching@sydney\/wp-json\/wp\/v2\/posts\/23045","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/educational-innovation.sydney.edu.au\/teaching@sydney\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/educational-innovation.sydney.edu.au\/teaching@sydney\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/educational-innovation.sydney.edu.au\/teaching@sydney\/wp-json\/wp\/v2\/users\/4332"}],"replies":[{"embeddable":true,"href":"https:\/\/educational-innovation.sydney.edu.au\/teaching@sydney\/wp-json\/wp\/v2\/comments?post=23045"}],"version-history":[{"count":12,"href":"https:\/\/educational-innovation.sydney.edu.au\/teaching@sydney\/wp-json\/wp\/v2\/posts\/23045\/revisions"}],"predecessor-version":[{"id":23242,"href":"https:\/\/educational-innovation.sydney.edu.au\/teaching@sydney\/wp-json\/wp\/v2\/posts\/23045\/revisions\/23242"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/educational-innovation.sydney.edu.au\/teaching@sydney\/wp-json\/wp\/v2\/media\/23046"}],"wp:attachment":[{"href":"https:\/\/educational-innovation.sydney.edu.au\/teaching@sydney\/wp-json\/wp\/v2\/media?parent=23045"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/educational-innovation.sydney.edu.au\/teaching@sydney\/wp-json\/wp\/v2\/categories?post=23045"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/educational-innovation.sydney.edu.au\/teaching@sydney\/wp-json\/wp\/v2\/tags?post=23045"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/educational-innovation.sydney.edu.au\/teaching@sydney\/wp-json\/wp\/v2\/coauthors?post=23045"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}Balancing innovation with responsibility<\/h2>\n
Remember your 6R\u2019s:<\/h3>\n
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\n READ<\/td>\n Actually read what the AI has generated. Don’t assume it’s automatically correct or relevant.<\/td>\n<\/tr>\n \n REVIEW<\/td>\n Check whether the output is truly what you’re looking for. Does it address the question or solve the problem?<\/td>\n<\/tr>\n \n RESEARCH<\/td>\n Verify the AI’s information. Cross-check facts and claims with credible sources before treating them as truth.<\/td>\n<\/tr>\n \n RECONCILE<\/td>\n Integrate AI outputs into your own work thoughtfully. Simply copying and pasting undercuts your learning.<\/td>\n<\/tr>\n \n REPROMPT<\/td>\n If the response isn’t accurate or you want more depth, ask again with clearer instructions or with higher specificity. Or start again.<\/td>\n<\/tr>\n \n RESPONSIBLE<\/td>\n Ultimately, you own your work<\/u>. If an AI-generated piece of code fails catastrophically or an AI-based design is flawed, it’s your responsibility.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n Implementation in practice<\/h2>\n
Implications for broader teaching practice<\/h2>\n
Using the 6Rs in your own teaching<\/h3>\n
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