Across universities worldwide, a quiet revolution is underway. Generative artificial intelligence (AI) tools such as ChatGPT, Copilot, DeepSeek and Gemini are being used to produce essays, summarise readings, and even conduct complex assignments.<\/p>\n
Generative artificial intelligence is a kind of AI that can handle a variety of creative tasks in diverse domains, such as arts, music and education.<\/p>\n
For many university teachers, this raises alarm bells about plagiarism and integrity<\/a>. While some institutions have rushed to restrict or support AI use<\/a>, others are still unsure how to respond. <\/p>\n But focusing only on policing misses a bigger issue: whether students are really learning. As an education researcher, I\u2019m interested in the topic of how students learn. My colleagues and I recently explored<\/a> the role AI could play in learning \u2013 if universities tried a new way of assessing students.<\/p>\n We found that many traditional forms of assessment in universities remain focused on memorisation and rote learning. These are exactly the tasks that AI performs best. <\/p>\n We argue that it\u2019s time to reconsider what students should be learning. This should include the ability to evaluate and analyse AI-created text. That\u2019s a skill which is essential for critical thinking. <\/p>\n If that ability is what universities teach and look for in a student, AI will be an opportunity and not a threat.<\/p>\n We\u2019ve suggested some ways that universities can use AI to teach and assess what students really need to know.<\/p>\n Reviewing studies of AI<\/p>\n Universities are under pressure to prepare graduates who are more than just knowledgeable. They need to be self-directed, lifelong learners who are independent, critical thinkers and can solve complex problems. Employers and societies demand graduates who can evaluate information and make sound judgements in a rapidly changing world. <\/p>\n Yet assessment (testing what students know and can do) tends to focus on more basic thinking skills.<\/p>\n Our research<\/a> took the form of a conceptual literature review, analysing peer-reviewed studies published since the release of the AI tool ChatGPT in late 2022. We examined how generative AI is already being used in higher education, its impact on assessment, and how these practices align (or fail to align) with Bloom\u2019s taxonomy. <\/p>\n Bloom\u2019s taxonomy<\/a> is a framework widely used in education. It organises cognitive (thinking) skills into levels, from basic (remembering and understanding), to advanced (creating and evaluating).<\/p>\n Several key patterns emerged from our analysis: <\/p>\n Firstly, AI excels at lower-level tasks. Studies show that AI is strong in remembering and understanding. It can generate multiple-choice questions<\/a>, definitions, or surface explanations<\/a> quickly and often with high accuracy. <\/p>\n Secondly, AI struggles<\/a> with higher-order thinking. At the levels of evaluating and creating, its effectiveness drops<\/a>. For instance, while AI can draft a business plan or a healthcare policy outline, it often lacks<\/a> contextual nuance, critical judgement and originality. <\/p>\n