What Have National Quality Assurance or Accreditation Bodies Recommended?

The three most influential bodies in informing academic integrity practices in the age of AI have been the UK’s Quality Assurance Agency (QAA), Quality and Qualifications Ireland (QQI), and Australia’s Tertiary Education Quality and Standards Agency (TEQSA), and they have all been careful to not be too prescriptive given the variety of different contexts in which their member institutions operate. For example, the UK’s Quality Assurance Agency (QAA, 2023a, 2023b) has emphasised that institutional policies need to be agile yet specific by, for example, codifying a clear distinction between what is acceptable and unacceptable GenAI use and then ensuring that students are aware of this. They also suggest that institutional policies should require that students explicitly acknowledge their use of GenAI tools and that ‘responsibility for the integrity of the submission lies with the student’ (QAA, 2023b, p. 2). In essence, the QAA recommends that while institutions work to redesign assessments for an age in which GenAI is ubiquitous and difficult to detect and control, they clearly state for students that it is academic misconduct to serendipitously submit GenAI output as if it was human-generated.

Quality and Qualifications Ireland (QQI, 2023) provides nineteen recommendations for educators, students, and institutional leaders like: lecturers should ‘design assessments which have strong validity’; students should ‘recognise the ethical concerns associated with their development and use’; and, institutional leaders should ‘ensure that there is up-to-date and regular training for all staff and students on academic integrity, AI, and assessment’. They also offer more detailed thirty-two guidelines to help each group bring those recommendations to life, including providing a lexicon of terms and frameworks for investigating suspected misuse of GenAI.

The Tertiary Education Quality and Standards Agency in Australia created their recommendations based on a survey of their institutions (TEQSA, 2024). The resulting TEQSA Toolkit suggests the proactive strategies that institutions can take to mitigate the key risks and challenges that GenAI poses to educational integrity, like establishing governance mechanisms (e.g., having an institutional GenAI strategy and plan guided by working groups), supporting and engaging students and staff in the implementation of the plan, and finally making changes to pedagogy and assessments to ensure that students are ‘actively engaged in their learning journey, and the knowledge and skills students require to graduate with their award have been assessed securely’ (p. 42). We recommend checking TEQSA’s toolkit as it is comprehensive and provides exemplars from institutions that could be helpful in informing your institutional policies.

What Do Leading National or Global Educational Associations Offer for Guidance?

In addition to quality assurance bodies, other national and international organisations offer assistance at a macro level. In the UK, the Joint Information Systems Committee (JISC) has used their research to develop comprehensive guidance about safe, ethical use of GenAI within education (JISC, 2024), which has been widely adopted by UK HEIs. They offer some more prescriptive advice like:

• don’t try to ‘ban’ specific technology or GenAI as a whole;

• describe acceptable and expected behaviours and provide examples; and,

• supplement general advice with tailored advice for the subject (JISC, 2024).

But they also reserve from weighing in on more controversial topics like the role of AI detectors, deciding to simply say ‘best practice for using such tools is still being considered and developed across our sectors, and we will aim to provide examples as these develop’. (np). This equivocation might be because around the same time, April 2023, a large group of UK universities called for a rejection of the automatic inclusion of AI detector results in the Turnitin similarity report used by most institutions, which resulted in Turnitin instead offering GenAI detection as opt in or opt out choices for institutions (Glendinning, Reference Glendinning and Baptista2025).

The Russell Group also released a set of principles for the twenty-four high-ranking research-intensive universities in the UK (Russell Group, 2023):

• universities will support students and staff to become GenAI-literate;

• staff should be equipped to support students to use generative GenAI tools effectively and appropriately in their learning experience;

• universities will adapt teaching and assessment to incorporate the ethical use of generative GenAI and support equal access;

• universities will ensure academic rigour and integrity is upheld; and,

• universities will work collaboratively to share best practice as the technology and its application in education evolves.

These principles offer some general benchmarking, but perhaps the most significant principle is the last; collaboration within and across the global sector of higher education must be a high priority to navigate the disruption that GenAI poses to teaching, learning, and assessment.

Should There Be an Institutional ‘Default’ Policy, or Should It Be Left Up to Individual Instructors?

Institutions should absolutely have a set of guidelines for best practice as well as an overarching policy and associated procedures, as they generally have had before GenAI. See Table 1 for examples of policies and guidelines from five English language institutions in the UK, Ireland, Australia, and Canada.

Table 1Example guidelines and policies from five higher education institutions

Table 1Long description

The table compares five universities’ approaches to generative-AI use, with columns for institution, policy, guidance, and AI-detection practices; University College London and King’s College London categorize or set principles for AI use, require student guidance, and reject detection tools because of privacy, bias, and fairness concerns; Atlantic Technological University and Deakin University treat undeclared AI use as unauthorized, advise consulting instructors, warn about bias and data protection, and do not rely on detection tools; and the University of Toronto defines unauthorized AI use under its conduct code, highlights risks of inaccuracy and privacy, and discourages submitting student work to AI systems for grading.

An easy way to update existing policies is to just say ‘it’s up to the course instructor whether GenAI is allowed or not’, but that really leaves it to the individual instructors. So, we suggest instead that the default guidelines situate academic integrity within teaching and learning, rather than in student conduct, which means acknowledging that GenAI can both amplify and hinder learning. One way for institutions to do this is to adopt the ‘two-lane assessment approach’ (Bridgeman, Liu & Weeks, Reference Bridgeman, Liu and Weeks2024) as a guideline or policy. This approach suggests there are open and closed assessments. Open assessments are conducted in unsupervised settings (think essay writing, homeworks) and since GenAI use cannot be controlled or detected in such settings, these assessments should be focused on facilitating student learning. Closed assessments are supervised and should be reserved for when we need to ensure and certify that a student has met certain learning outcomes or benchmarks. So, if this approach was adopted as a guideline, it would suggest to instructors that they shouldn’t prohibit GenAI use on ‘open assessments’ (those completed in unsupervised settings) but allow it with acknowledgement. As a policy, the two-lane approach would prohibit instructors from banning GenAI on open assessments, like they do at the University of Sydney. We would still encourage instructors to provide clear instructions to students about the expected use of AI in open assessments with a rationale for using in this way.

Another way for institutions to emphasise the teaching and learning approach is to make it policy that instructors and students must disclose their use of GenAI in their work, and if their disclosed actions undermine the purpose of the work (i.e., teaching and learning), they’re given a chance to correct the mistake rather than be disciplined for ‘dishonesty’. This doesn’t mean there should never be consequences. Rather, we suggest following the educational enforcement pyramid developed by Ellis and Murdoch (Reference Ellis and Murdoch2024) because it provides a useful re-imagining of institutional strategies for handling academic misconduct. Specifically, the authors suggest that institutions map their educational interventions and penalties appropriately to student contexts; the bottom of the pyramid being where students are both willing and able to do the work of learning and are given advice and support by their institutions, while the top of the pyramid would be where students are neither willing nor able to do the work of learning and are sanctioned most severely by their institutions.

Should Institutional Policy Prohibit AI Detection?

It’s essential in a teaching and learning approach that educators redesign their assessments and then prioritise detecting learning, not detecting cheating (more on this in the next section). Nevertheless, we get the inclination to detect. After all, text similarity detectors proved quite useful in helping faculty identify plagiarism. Also, we acknowledge that many faculties are not given the time, training, and support to make pedagogical or assessment changes, and others may choose not to incorporate GenAI or make changes for other reasons, like it is already a well-designed assessment or allowing GenAI would undermine learning (Gustilo, Ong & Lapinid, Reference Gustilo, Ong and Lapinid2024). However, when unsupervised assessments don’t change and students offload their learning to machines, degree integrity will be compromised (Newton & Draper, Reference Newton and Draper2025), especially when instructors refuse to respond to the offloading due to a (mis)perception that they would be ‘policing students’ (Lynch et al., Reference Lynch, Salamonson, Glew and Ramjan2021).

This misperception that addressing cheating is ‘policing’ stems in part from not knowing how to respond and an over-emphasis on punishment (as we discussed in Section 1), but the addition of GenAI detectors hasn’t helped. Numerous studies have found that the detectors can have a high likelihood of false positives – that is, identifying human writing as AI generated (Dalalah & Dalalah, Reference Dalalah and Dalalah2023; JISC, 2024) – especially in writings by those students who speak English as an additional language (Liang et al., Reference Liang, Yuksekgonul, Mao, Wu and Zou2023). These studies feed into the ‘policing’ narrative even while other researchers have found that some detectors can be very accurate with no bias against ELL students (e.g., Jiang et al., Reference Jiang, Huang and Li2024). The problem is that the studies have significant methodological differences and vary in the detection tools tested, so the effectiveness and/or biases of the detectors are not clear. For example, Liang et al. (Reference Liang, Yuksekgonul, Mao, Wu and Zou2023) contrasted the AI detection of English test essay outputs by 91 Chinese students who spoke English as an additional language with 88 eighth-grade student essays by speakers of English in the United States. Dalalah and Dalalah (Reference Dalalah and Dalalah2023) constructed their analysis of false positives based on fifty extracts from peer-reviewed journal articles published prior to 2022 with fifty new extracts produced by ChatGPT.

So, the question – should institutions prohibit AI detection – is difficult to answer. We can see in Table 1 that respected institutions have taken different approaches, and even as authors, we do not agree on the answer. Mary, for example, believes that institutions should prohibit AI detection tools given that some have been shown to be problematic and the results of all can be misused. Tricia, on the other hand, believes that institutions have a responsibility to provide instructors with reliable tools that, when used along other pieces of data, may help the instructor validate learning. Regardless, in institutions where AI detectors are allowed: (a) institutional policy should prohibit instructors from acting as judges and juries on their own without invoking an institutional process; (b) the institution must provide a reliable and vetted tool (and prohibit instructors from using freely available ones found online); (c) a report from an AI detector tool – even a good one – should not be permissible as the only piece of evidence to ‘prove’ a student cheated; (d) any tool used must be proactively disclosed to students; and (e) the detector output is best used not as a ‘gotcha device’ but as context for a validating learning conversation that the instructor (or someone else) has with the student. In the end, institutions should promote teaching and learning with integrity and the goal is always to enable the students to demonstrate their achievement of the learning outcomes, regardless of what the ‘detectors’ say.

What Frameworks Exist to Guide Us in Developing Our Institutional Policy?

There are a number of useful policy frameworks developed prior to GenAI that are still relevant. One of the most widely used is the five principles of exemplary academic integrity policy: access, approach, detail, responsibility, and support (Bretag et al., Reference Bretag, Mahmud and Wallace2011). Considering these five elements, GenAI policies must be fully accessible in terms of both location and format, should take an educational approach, need to have sufficient detail to help students gain understanding, involve establishing responsibility for all stakeholders (in other words, commitments for the institution, faculty, and students in terms of GenAI), and, finally, require structures of support, including teaching resources and time for learning, in order to be effective.

The use of Universal Design for Learning (UDL), a system for embedding inclusion into all student-facing documents and resources (CAST, 2018), is also strongly recommended to ensure policies provide contextual background, identify key concepts, assist with information processing, and promote the application of knowledge. Building in UDL principles across integrity policies and assessments helps to ensure they are in a format that is fully acceptable to all students (Davis, Reference Davis and Eaton2024). Applying a UDL framework can surface, for example, that GenAI-powered tools used in grading, misconduct detection, and proctoring may perpetuate biases and undermine fairness (Baker & Hawn, Reference Baker and Hawn2021), often disadvantaging students from underrepresented, neurodivergent, or multilingual backgrounds. These challenges are set to intensify with the rise of brain–computer interfaces, neuro-monitoring, and adaptive cognitive tools, which may redefine how knowledge and performance are measured. Any guidelines, policies, and procedures adopted must work to safeguard both intellectual honesty and educational justice. Academic integrity in this context must extend beyond preventing cheating to ensuring that assessment systems are valid, transparent, procedurally just, and ethically accountable.

Based on the aforementioned, the following questions can be used as a framework to help institutions develop GenAI guidelines or policies that address important ongoing considerations:

• Who is responsible for validating learning and certifying it?

• How can students contribute to guidelines or policies?

• How are integrity breaches defined?

• How can students be transparent about their use of GenAI? (e.g., declaration/acknowledgement/ permission form)

• How is appropriate use of GenAI defined? (e.g., using GenAI as a tool)

• How is GenAI misuse defined?

• Should we allow GenAI use by teachers in grading, etc.?

How Do We Include Student Voice in Developing Guidelines, Policies, and Procedures?

We highly recommend that student voices are included, along with instructors, in the development of GenAI and academic integrity guidelines, policies, and procedures. The importance of incorporating the student voice in any academic integrity work has been strongly recognised (Davis, Reference Davis2022). Listening to and working with students has become even more crucial as a way to keep up with their practices, experiences, and employability needs with GenAI. For example, Johnston et al.’s (Reference Johnston, Wells and Shanks2024) survey of students demonstrates an approach to incorporate students’ relevant practices (such as using GenAI as a personal tutor) and current concerns (such as ensuring they can acquire GenAI skills for jobs) into university policy. In Ireland the N-TUTORR (National Technological University Transformation for Recovery and Resilience) project set out to engage students as champions of integrity and inclusion and as partners in innovation and change (N-TUTORR, 2024). King’s College London (2024) advises that educators start by having conversations about GenAI with peers, familiarising themselves with the GenAI research landscape, experimenting with GenAI, analysing the impact on the course, reviewing assessments, and then communicating with students. This approach is in line with the Russell Group (2023) principles of developing both student and staff GenAI literacy (mentioned in regard to the meso level earlier).

How Do We Ensure Our Policies and Procedures Stay Relevant?

To serve the purpose of informing students and faculty, especially in the world of quickly evolving GenAI technologies, academic integrity and GenAI policies need to be visible, easy to access, and regularly updated. These policies should not be hidden from students within long documents or on pages that their attention is not drawn to (Pecorari, Reference Pecorari, Belcher and Hirvela2001; Stoesz et al., Reference Stoesz, Eaton, Miron and Thacker2019). Regular reminders are needed and the guidance and training for students should also be taken by staff, so everyone is informed of what students are being advised to do. Policy and procedures should be regularly reviewed to ensure that students do not get caught by policies that no longer apply. For example, if a pre-GenAI policy states that students cannot use electronic tools for assessment while at the same time they are encouraged to use GenAI, the policy causes confusion for both faculty and students. Policies can be updated with simple references to GenAI. At the University of California, San Diego, for example, the pre-GenAI policy said that handing in work done by someone else was a form of dishonesty; the post-GenAI policy says that handing in work done by someone or something else (i.e., artificial intelligence) is a form of dishonesty. Regardless of whether GenAI misuse is weaved into existing definitions of integrity violations or listed as a separate category, misuse of GenAI tools needs to be clearly defined, monitored, updated, and often communicated at both the institutional and course levels.

Should We Mandate Different Policies at Different Levels of Study?

It does seem reasonable to scaffold in responsible GenAI use. In an introductory physics, chemistry, or math class, we may want to prohibit use by relying on secure assessments because students need to develop some foundational knowledge before they can effectively input useful prompts and evaluate outputs for accuracy and bias. On the other hand, the higher a student progresses, the more responsibility they have for authenticity, originality, and research rigour and so GenAI misuse at the doctoral level is likely to be a greater concern. To use King’s College London (2025) as an example: doctoral students may use GenAI tools in their thesis writing process, but should be cautious in their use, paying particular attention to data protection and confidentiality as part of research integrity, declare all use, and take responsibility for the output they incorporate (i.e., the machine’s errors are actually my errors). Students may use GenAI as an assistive tool to correct language, rewrite, paraphrase, or translate parts of the thesis, but not to correct content or generate a literature review, as this would compromise their originality, contribution to the literature, and their ability to discuss and defend their own thesis (King’s College London, 2025). The guidance evidently aims to separate ethical language correction/improvement from unethical content correction/improvement in GenAI use, which is clearly an important distinction, but can be hard to fully apply in practice and requires more nuance according to the type and purpose of writing required. It has been recognised for some years that using tools for paraphrasing or translating can result in a lack of human authorship and significant concerns for academic integrity (Groves & Mundt, Reference Groves and Mundt2021; Roe & Perkins, Reference Roe and Perkins2022); thus, policy guidance about use of GenAI at each level needs to be accompanied by ongoing monitoring and discussions with students.

What Tools or Frameworks Exist to Help Me Develop My Course-Level Policy on AI Use?

The AI Assessment Scale (AIAS) by researchers in Vietnam, Australia, and Singapore (Perkins, Furze & Roe, Reference Perkins, Furze and Roe2024) has been a widely adopted tool. The AIAS presents five levels of GenAI use to be employed according to the learning outcomes in assessment: no GenAI, GenAI-assisted idea generation, GenAI-assisted editing, GenAI task completion with human evaluation, and full GenAI. The model is being widely used with adaptations to different global and educational contexts using traffic lights, Bloom’s taxonomy, competency scales, and other formats (Furze, Reference Furze2024). We would caution all instructors, though, that the ‘no GenAI’ option can only be applied in a particular assessment or course where the assessments are secure. We also recommend that if any GenAI use is allowed, it is coupled with a disclosure, so the use is transparent and enables the instructor to have conversations with the students if their use goes awry.

Should I Allow Students to Use GenAI or Not?

Phillip Dawson would argue that before worrying about allowing or not allowing GenAI, instructors should first ensure that they have a valid assessment, which means evaluating if we are ‘assessing what we mean to assess’.Footnote ²³ Or, more bluntly, Phill says: ‘If an assessment is no good at being an assessment, don’t worry about AI. Fix the damn assessment.’Footnote ²⁴

Once you’re assured that you have the right assessment, each instructor needs to determine if the use of LLMs hinders or amplifies a student’s mastery of the learning outcomes and can that be measured. Bridgeman, Liu, and Weeks (Reference Bridgeman, Liu and Weeks2024) suggest that to make this decision, instructors should distinguish between summative assessments (assessments of learning) and formative assessments (assessments for and as learning); the former may be more likely need to be secure where the use of aids is known (even if that aid is GenAI), whereas the latter is unsupervised and therefore vulnerable to GenAI misuse. Even when we decide that using GenAI is useful on some assessments, we may want to design ‘hurdle assessments’ to ensure that a student has knowledge and skills even without the assistance of an LMM (Bridgeman, Liu & Weeks, Reference Bridgeman, Liu and Weeks2024). This strategy creates an essential measure to prevent students from graduating based on their ability to offload to GenAI, rather than their ability to add value to a machine-augmented human world.

What Kind of GenAI Use Supports Learning?

Ultimately, we want to ensure that even in a GenAI world, students continue to develop their crucial critical thinking abilities, particularly since research has demonstrated that over-reliance on GenAI may reduce cognitive development (Zhai, Wibowo & Li, Reference Zhai, Wibowo and Li2024). This usually means teaching students how to use the tools as assistants rather than replacements so that humans remain the authors with active knowledge and responsibility for the output. In some courses, it might be okay to teach the students how to brainstorm with the LLM to develop topic ideas or research strategies. In other courses, it might be acceptable for the student to use it at the proofreading stage so that they can focus more on understanding and less on linguistic accuracy. Still in other classes, perhaps GenAI can be used to support learning by being trained as a course tutor, providing practice quizzes, or giving timely feedback on a draft assignment. If the students are taught to share and reflect on their exchange with the bots, then they can learn about responsible and ethical use. Instructors could teach responsible use by having students evaluate GenAI outputs for bias and accuracy or comparing outputs to existing published sources (Acosta-Enriquez et al., Reference Acosta-Enriquez, Arbulú Ballesteros and Arbulu Perez Vargas2024). If students are encouraged to focus their attention on their responses to GenAI outputs, fact-checking, examining bias, and assessing value, then this can be a positive learning approach. And yes, students could still do too much offloading and lie about the extent of their use, which gets us back to the ‘two lane assessment’ approach; it’s most important that instructors differentiate when GenAI use can support learning versus when it interferes with the students’ honest demonstration of their learning.

How Do I Stop Students from Using GenAI on Assessments When It Will Undermine the Purpose?

According to Bertram Gallant and Rettinger (Reference Bertram Gallant and Rettinger2025), you can implement strategies to address the underlying reasons why students cheat. So, minimise misunderstandings by being clear about what is allowed and what’s not (e.g., by using Perkins et al.’s Artificial Intelligence Assessment Scale) and connecting those guidelines to the learning goals. Reduce temptations by allowing for multiple drafts, second chances, and/or flexible deadlines so that students, when pressed for time or feeling unable to do the assessment, might just try their best or communicate with you rather than use unauthorised GenAI tools. And, raise intrinsic motivations for learning by considering alternative grading strategies to reduce performance pressures, or even just adjust grading rubrics so that GenAI produced work earns a failing grade.

Otherwise, as Bertram Gallant and Rettinger (Reference Bertram Gallant and Rettinger2025) also note, the only way to really stop students from using any unauthorised aids on an assessment is to secure that assessment. As Phillip Dawson (Reference Dawson2021) says, ‘assessment security begins where academic integrity ends’. What he means is that we can implement all of the strategies to make ‘cheating the exception and integrity the norm’ (Bertram Gallant, Reference Bertram Gallant2017), like raising students’ intrinsic motivations, enhancing their self-efficacy, and reducing stress and pressure. However, because some students will still cheat, assessments that determine if a student should progress in their programme or graduate with a credential must be secure. But securing doesn’t have to look like an in-person, blue-book exam (which isn’t very secure, by the way, if those in charge aren’t implementing security measures like checking for AI-enabled glasses or student identification). Other options for raising security on assessments include oral assessments and flipped classrooms. Oral assessments do not have to be as complicated as you might think. They could mean taking time to talk with students instead of marking written exams; orally following up with students for about 15 minutes after written assessments; or implementing debates, simulation activities, and interviews (see Ward et al., Reference Ward, O’Riordan and Logan-Fleming2023 for example). In flipped classrooms, the majority of assessments are completed in the presence of others so there is accountability and transparency in the methods of their completion, and thus simply more challenging to fake.

How Do I Know When Students Have Breached Academic Integrity by Misusing GenAI?

This is the question of the day. Before it can be answered, you need to define what ‘misuse’ is. For one professor, using GenAI might be permitted or required, and for another, the same use might be labelled as ‘misuse’. Is it misuse if it’s used at all, or only misuse if they use it without citation or acknowledgement? Is it only misuse if it’s used to author versus to assist?

Then, if you determine that there are ‘misuses’ that will undermine the purpose of the assessment, then you must secure it. It’s a waste of your time to ban GenAI on an unsupervised assessment because you’ll only then have to spend time trying to detect who misused it. If for some reason you cannot secure the assessment, then we recommend that you allow GenAI use but ask students to complete a GenAI Disclosure. If they disclose they didn’t use it or they used it in a way that sounds dubious to you, then use it as an opportunity to validate learning rather than detect cheating. What do we mean by that? It means talking with the student to see if they learned or didn’t.

After all, your instinct could be wrong. The very things that ‘scream’ AI use – ‘it’s vague, doesn’t address course readings, is a summary instead of an argument and lacks sources’ – are the same things that occur in a paper that was badly constructed by a human. So, talk to your student about their process of completing the assessment, view their document version history to explore their process, ask them about the choices they made, and give them a chance to show you that they learned. If a student can talk through the assessment with you, then that might assuage your concerns. And, as we discussed already, be cautious: don’t rely on AI Detectors alone which can lead to unnecessary accusations and unfairness (Bassett, Reference Bassett2025). Only use a detector if one is provided to you by your institution, and, if all of the other points of data suggest that the assignment was human-generated even though the detector says ‘AI generated’, believe the former.

Are Our Courses and Their Associated Learning Outcomes Still Relevant in the Age of GenAI?

There may be some courses that are now irrelevant because they teach outdated skills or focus on memorising content (that doesn’t need to be memorised). Or, maybe there are some courses that just need some reworking with a change to their learning outcomes or assessment methods. While individual instructors can do this check themselves (as already advised), we believe that this should be done as a collaborative exercise. Of course, there will be disagreements and perhaps even arguing over whose course is ‘important’ or ‘irreplaceable’, so this work is not easy, but it must be done. The conversation should begin with the foundation – the programme’s or department’s learning outcomes. Do they need to be updated at all? Then, we can ask which courses and assessments help students master those learning outcomes and which need to be reimagined or archived.

Are Our Courses Meaningfully Connected?

To really revise learning and assessing in the age of GenAI, we need to look at learning as a continual process, not anchored to a particular term or course. Too often we’ve heard instructors suspect GenAI use because the students used a method or concept not taught in that particular class, only to find out that the student was applying their learnings from another class. We want students to do this because the ability to apply existing knowledge to new situations or adapt one skill for use in a new context is the hallmark of lifelong learning. Programmes and departments should be looking at all of their courses to ensure that there is some connecting thread or purpose between all of them, especially those that are required for a major, for example. Or, at the very least, examining these courses and being transparent about what is taught in them so that every instructor has a sense of the bigger picture in which their course fits.

Do Our Courses Deliver on the Promise of Developing Durable Human Skills?

One of the healthy responses to GenAI has been a re-evaluation of the importance of ‘soft skills’ which are the durable human skills graduates need for the future.Footnote ²⁵ For far too long, colleges and universities have relied on the ‘hidden curriculum’ for such skill development. Our students develop interpersonal and collaboration skills because we make them work in groups. Our students develop communication skills because we have them produce written and oral outputs like research papers or presentations. Our students hone their problem-solving skills by simply navigating our complex bureaucracy! Instead, we need to be more intentional and strategic to ensure that throughout the degree, the student is progressing in these skills and we can validate that progress. After all, it is likely the strength of these skills that will enable our human students to bring added value to an AI world.

What Are the Benchmark Moments Where We Can and Should Administer Secure Assessments?

The answer to this question will differ by disciplines and learning outcomes. However, we can provide some general guidance thanks to Bridgeman, Liu, and Weeks (Reference Bridgeman, Liu and Weeks2024) who suggest the following:

• incorporate a capstone unit that assesses programmatic learning outcomes;

• use common formative assessment tasks across multiple courses;

• use a common summative assessment across multiple units of study at third year; and,

• at milestones, incorporate a useful hurdle assessment to confirm progression to the next level of the programme, suggest remediation, or validate achievement.

How Do We Measure Student Learning?

Most higher education institutions continue to be structured around a credit-hour based system in which courses are scheduled to begin and end on specific dates with required measurements of how many hours a student spends ‘learning’. In synchronous classes (whether physical or virtual), the hours spent learning is usually calculated by time in class as well as time spent on assignments/assessments. In asynchronous classes, ‘learning’ is measured by number of hours of ‘direct faculty instruction’ (think recorded video lectures) and time spent on assignments/assessments.Footnote ²⁶ This, of course, is an archaeological artefact of the agricultural and industrial ages, and although it may have made sense at the time, it no longer makes sense in the twenty-first century when: information and knowledge can be accessed 24/7; there is more diversity in the schedule of a ‘day’s work’; and, people can remain connected to an online learning environment even as they move quickly between physical spaces (Thelen, Reference Thelen2022). So, fundamentally institutions of higher education need to move away from measuring learning by time in seats to measuring learning by competencies. Competency-based education is not new, but it has not yet been widely adopted. We think that in an AI-augmented world, it’s not only critical that it be considered but possible. According to Levine and Patrick (Reference Levine and Patrick2019), a competency-based education requires seven elements:

1. 1. ‘Students are empowered daily to make important decisions about their learning experiences, how they will create and apply knowledge, and how they will demonstrate their learning.

2. 2. Assessment is a meaningful, positive, and empowering learning experience for students that yields timely, relevant, and actionable evidence.

3. 3. Students receive timely, differentiated support based on their individual learning needs.

4. 4. Students progress based on evidence of mastery, not seat time.

5. 5. Students learn actively using different pathways and varied pacing.

6. 6. Strategies to ensure equity for all students are embedded in the culture, structure, and pedagogy of schools and education systems.

7. 7. Rigorous, common expectations for learning (knowledge, skills, and dispositions) are explicit, transparent, measurable, and transferable.’ (p. 3)

Moving from the current dominant model to competency-based will not be easy, but it’s far past time.

Is Online Learning Still Viable for Both Educational Access and Integrity?

While certainly there are benefits to online learning – primarily access – there are not many benefits to online, asynchronous assessments (whether used for in-person or online classes). To secure assessments in such a space, there is a stronger requirement to provide authentication, ensure individual responsibility for actions, and assure academic integrity in assessment (Khan et al., Reference Khan, Sivasubramaniam, Anand and Hysaj2021a). While we can deploy pedagogical and other strategies to attempt to make cheating the exception and integrity the norm (Bertram Gallant & Rettinger, Reference Bertram Gallant and Rettinger2025), higher education still has the responsibility to ensure the integrity of assessments. So, the answer to this question is nuanced. Yes, online learning is viable, but we must begin to question – and tease out – whether online, asynchronous assessment is viable for ensuring degree integrity. Newton and Draper (Reference Newton and Draper2025), among others, would argue that it’s not. Therefore, we have to figure out how to both provide access and protect integrity. One way to do that might be to establish and utilise assessment centre networks so that the majority of students will live reasonably close to an in-person assessment option. Another way is with the benchmark or milestone assessments recommended by Bridgeman, Liu, and Weeks (Reference Bridgeman, Liu and Weeks2024); a student may get convenience and access through the learning portion of their experience, but when it comes time for assessment at just a few points throughout their programme (rather than in every class), it will be secure and it may have to be inconvenient. We’re always told that colleges and universities have to prepare students for the ‘real world’; what’s more ‘real world’ than having to do things that are challenging and inconvenient?

How Can We Focus on Human-to-Human Learning Experiences?

To avoid the ‘dead university’ where GenAI is used to both author and grade student work, a re-focus on human interaction is needed. After all, students do not necessarily need higher education institutions to learn; people can learn anytime, anywhere. They do need us for certification and usually for opportunities to learn with other humans, and particularly to learn in environments they cannot otherwise access (e.g., laboratories). In other words, they need us for a real (not virtual) learning playbox. So, especially in large institutions with big classes, we need to leverage GenAI to find efficiencies so that we can flip the classrooms into active, engaged learning environments. For example, perhaps we can use GenAI to deliver content and provide just-in-time tutoring so that the instructor can meet with students in smaller discussion or application sections.

How Do We Support Instructor Efforts to Maintain Academic Integrity in the Age of GenAI?

GenAI tools are designed to generate output and to automate cognitive tasks, which can be detrimental for facilitating and validating learning with integrity. Thus, institutions must provide instructors with time, training, and support to redesign courses and assessments though summer stipends, course release, instructional designers, or peer mentors. They can also create infrastructure to assist instructors. For example, is it a good use of instructor time to administer and proctor tests? We suggest that it isn’t. Instead, when tests are appropriate, instructors should be able to send their students to a computer-based assessment centre that is staffed by professional proctors and allows mastery-based, secure assessments (Zilles et al., Reference Zilles, West, Herman, Bretl, Lane, Zvacek and Uhomoibhi2019). Also, institutions can help instructors design 24/7 learning support to students through customised courses or skill-related (e.g., writing) AI tutors. The point is that institutions need to support instructors in this massive change and not expect them to rebuild the proverbial planes as they’re flying them.

How Do We Teach with AI with Integrity?

The integration of GenAI tools into coursework presents a paradox: it can either erode student learning or amplify it. For educators, the question is not simply ‘Is this cheating?’ but rather ‘How do we ensure meaningful learning with integrity?’ This may mean, sometimes, banning GenAI and securing assessments if the learning outcomes would be hindered by GenAI use. When it can be used, we should cultivate ethical use by designing assessments that treat GenAI as a thinking partner rather than a shortcut, and explicitly require transparency, reflection, and explanation in and of that use. This means that instead of simply submitting a final product, the student would reflect on how they approached the task, why they made certain decisions, and what role AI played in shaping their process. In this case, AI becomes a cognitive scaffold, not a crutch for thought; a prompt for deeper reflection which also keeps the human in the centre of the learning experience. For instance, rather than only submitting final code, students in a third-year computer science course might be asked to include a process log or reflective statement outlining how they used AI tools, what prompts they issued, and how they validated AI outputs. This not only demystifies the black box of AI-assisted work but fosters integrity and accountability. Such approaches have already shown promise in recent pilot programmes that embed AI literacy within assessment strategies (Eaton, Reference Eaton2024; Nikolic et al., Reference Nikolic, Daniel and Haque2023; Perkins, Furze & Roe, Reference Perkins, Furze and Roe2024).

To support this shift, Khan (Reference Khan and Eaton2024a) proposes a practical five-part decision-making framework to guide when and how to integrate generative AI into teaching, learning, and assessment:

1. 1. Student Maturity Level: Teaching with AI must be aligned with the learner’s cognitive development, using tools progressively based on students’ ability to reflect and self-regulate.

2. 2. Learning Outcomes and Workplace Readiness: AI should support – not substitute – the acquisition of disciplinary knowledge and future-focused competencies.

3. 3. Assessment Design and Cognitive Offloading: Educators must ask whether AI use enhances or inhibits the development of critical thinking and problem-solving skills.

4. 4. Accessibility and Equity: The framework cautions against reinforcing inequalities by assuming all students have equal access to AI tools or know how to use them responsibly.

5. 5. Ethical Guardrails: Usage must be framed within discussions of bias, misinformation, authorship, and the value of human judgement.

Embedding this framework into curriculum design enables educators to remain agile and intentional about the responsible use of GenAI in education, which must go beyond technical skills to include digital ethics, epistemic awareness, and agency. Students must learn not only how to prompt, but when to pause, recognising when GenAI aids learning and when it obscures it. Educators, meanwhile, must model the same reflective practice, acknowledging GenAI’s affordances and limits while preserving human-centred pedagogy.

As generative technologies evolve, institutions will need to align GenAI use with broader educational aims: not just to streamline outputs, but to nurture inquiry, resilience, and lifelong learning. If done thoughtfully, teaching with GenAI will not undermine academic integrity but reinforce it, anchoring it in the very processes of critical thinking and self-awareness that define meaningful education.

How Do We Teach about GenAI with Integrity?

The ethical implications of GenAI are far too significant to be confined to computer science or philosophy departments. Students across disciplines such as business, education, healthcare, or the arts are already engaging with GenAI tools, often without fully grasping their ethical, legal, or social implications. As GenAI becomes more embedded in educational practice and professional workflows, it is critical to embed structured ethics education across curricula.

This is not just about raising awareness of GenAI’s potential biases or limitations; it is about equipping students with the cognitive tools to interrogate its design, use, and impact. UNESCO’s AI Competency Framework for Teachers underscores the importance of preparing educators and learners with a human-centred, ethics-driven mindset. It calls for embedding five key dimensions that include AI ethics and human agency into teaching strategies to support professional growth and lifelong learning (Miao & Mutlu, Reference Miao and Mutlu2024).

Developing this mindset should be part of cultivating the broader, sustained human skill of ethical reasoning. For example, students could be introduced in their first year to a structured ethical decision-making model (Christensen Hughes & Bertram Gallant, Reference Christensen-Hughes, Bertram Gallant and Bretag2016) and then be guided to apply this model across different courses and disciplines. Such an approach ensures that ethical reasoning becomes a consistent and reinforced practice, rather than an isolated topic. Educators can begin this process by prompting questions such as:

• What assumptions or biases might this GenAI tool carry?

• Who benefits and who is excluded from its design or outputs?

• If I use this tool to generate content, who owns the intellectual property?

• Does using GenAI enhance or diminish my understanding of the topic?

• Does using GenAI fabricate my knowledge and abilities?

Such questions turn ethical reasoning into a lived practice, encouraging students to explore GenAI not only as a tool but as a sociotechnical system shaped by human values. Empirical research from cyber ethics courses has shown that sustained engagement with ethical theories and real-world dilemmas can significantly shift student perceptions and deepen their understanding of responsibility, fairness, and transparency in GenAI use (Khan, Venugopal & Oroumchian, Reference Khan, Venugopal, Oroumchian, Arabnia, Deligiannidis, Tinetti and Tran2021b).

Moreover, stand-alone ethics modules, when strategically infused into the core of technology and applied sciences programmes, can foster moral agency, improve decision-making, and help students internalise digital responsibility (Khan, Reference Khan2017; Melo & de Sousa, Reference Melo and de Sousa2017). These courses are not just philosophical add-ons but foundational to the development of responsible GenAI practitioners.

Ultimately, as the UNESCO Global Education Agenda 2030 urges, the future of ethical AI education must be inclusive, rights-based, and aligned with sustainable development. Teaching about AI ethics is no longer optional; it is a pedagogical imperative to prepare future developers, designers, and decision-makers for an AI-driven world. These ethical considerations form the philosophical backbone of AI education. Yet to truly empower students as competent and critical users, this ethical grounding must be paired with applied AI literacy – an area explored in the next sections.

How Do We Shift from Policing to Ethical Empowerment?

Integrity in the age of GenAI must move beyond a reliance on detection and punishment. Integrity is increasingly recognised as a disposition such as an ethical commitment to truth, effort, and autonomy. This paradigm shift urges educators to design learning experiences that develop ethical agency and moral character rather than merely enforce compliance.

Reflective practice offers one such pathway. Drawing on evidence from language and teacher education, guided journaling has been shown to build rapport, promote engagement, and encourage honest self-expression, particularly when students are invited to share personal experiences and reflect on their learning processes (Chan & Aubrey, Reference Chan and Aubrey2021; Khan & Venugopal, Reference Khan and Venugopal2019). Inspired by these successes, similar approaches are now being adapted in GenAI-integrated classrooms.

One such adaptation involves the use of ‘Decision Journals’, where students can document their choices across the research and creation process. Typical prompts could include:

• When were you tempted to over-rely on GenAI?

• How did you verify the accuracy of GenAI-generated content?

• What trade-offs did you consider between efficiency and comprehension?

In tandem, institutions are exploring assessment redesigns that encourage transparency rather than prohibition. For instance, a growing number of universities now ask students to submit not only final outputs but also process reports outlining (Black & Tomlinson, Reference Black and Tomlinson2025; Perkins, Furze & Roe, Reference Perkins, Furze and Roe2024):

• When and why they used GenAI tools;

• How they evaluated the limitations or biases in the output;

• What personal contributions they made in editing or revising the GenAI-generated content.

This method is proving effective in reinforcing student ownership over their work while reducing instances of misconduct such as forms of contract cheating. Early feedback suggests that students feel more confident distinguishing between GenAI assistance and GenAI replacement, and are more engaged in ethical dialogue with instructors.

How Do We Develop Students’ Meta-cognition?

In the years ahead, the ability in education to reflect on one’s thinking will be just as essential as mastering disciplinary content. In GenAI-rich educational settings, meta-cognition serves as a critical buffer against automation-induced detachment from the learning process. Without it, students risk accepting generative content uncritically, eroding not only academic integrity but also their development as independent thinkers. And this is not just critical for learners. In the world outside academe, the metacognitive skills to ‘monitor, regulate, and control one’s own cognitive processes’ will be critical to being able to effectively collaborate with GenAI tools (Sidra & Mason, Reference Sidra and Mason2024). So, meta-cognition is both a necessary skill for students and one of those durable human skills helpful in an AI-augmented world.

To develop this meta-cognition, we can borrow from consequentialist perspectives, such as those of John Stuart Mill, that call on learners to reflect on the broader impacts of their actions. Educators are thus being encouraged to embed structured opportunities for this reflection into coursework. Emerging strategies may include:

• assignments that require justification of GenAI use;

• journaling that explores how GenAI output was verified or challenged; and,

• class discussions where students analyse the ethical implications of GenAI-generated responses in peer work.

Several institutions have incorporated meta-cognitive reflective elements directly into assessment rubrics, transforming ethical reasoning from an abstract discussion into an evaluated learning outcome (An, Yu & James, Reference An, Yu and James2025; Combrinck & Loubser, Reference Combrinck and Loubser2025). The result: students develop not only skills but also judgement and are more likely to engage critically and ethically with GenAI as it continues to evolve.

As GenAI becomes increasingly embedded in all aspects of academia, the educator’s role will be to model, scaffold, and assess responsible use, not to outlaw it. The shift from surveillance to empowerment is already underway (Dawson, Reference Dawson and Eaton2024; Feng et al., Reference Feng, Luxton-Reilly, Wünsche and Denny2025; McKenzie, Reference McKenzie2021). This shift does not mean abandoning traditional approaches such as clear institutional rules, close observation for the certification of knowledge and abilities, and responding to integrity breaches when they occur, because these remain essential for upholding academic integrity. Rather, it reflects a growing recognition, supported by the UNESCO AI Competency Framework for Teachers (Miao & Mutlu, Reference Miao and Mutlu2024) and literature such as Macfarlane, Zhang, and Pun (Reference Macfarlane, Zhang and Pun2012), Christensen Hughes and Bertram Gallant (Reference Christensen-Hughes, Bertram Gallant and Bretag2016), Combrinck and Loubser (Reference Combrinck and Loubser2025), and others that such measures are most effective when balanced with strategies fostering student agency, reflective capacity, and ethical literacy.

What Is AI Literacy?

AI literacy encompasses a broad set of interdisciplinary competencies that go beyond technical familiarity. It includes:

Students must be able to see AI not as an oracle but as a system shaped by human values, economic incentives, and cultural biases. Empowering students with this understanding fosters agency and autonomy in how they approach their academic and professional work.

How Can We Teach AI Literacy?

AI literacy cannot be confined to computer science or data analytics courses. Its implications stretch across the curriculum such as media studies, law, education, engineering, and so on. A communications student using AI to generate headlines must understand media bias and political framing. A nursing student reviewing AI-assisted diagnostics must critically assess patient safety and embedded medical bias in the systems to ensure human accountability for determining diagnoses and treatments. A design student working with GenAI tools must evaluate intellectual property rights and creative integrity.

Forward-looking institutions are embedding discipline-specific AI literacy modules into existing coursework (Ngo & Hastie, Reference Ngo and Hastie2025). These could include:

• simulated decision-making tasks involving AI-generated content;

• assignments that require the student to critically compare their writing to the writing generated by the machine; and

• collaborative exercises where students role-play as ethical reviewers of AI outputs.

Such integrations help students ask not just ‘Can I use AI here?’ but ‘Should I?’ – and ‘What are the academic and ethical implications if I do?’

As students progress, more capabilities of AI can be introduced in a scaffolded way, to help them learn without cognitive overload (Long & Magerko, Reference Long and Magerko2020). Students could be challenged to engage with AI through disciplinary and ethical frameworks; for example, applying professional codes of conduct, evaluating AI reliability in subject-specific contexts, or critiquing the role of generative tools in research and communication.

By their final year, students should be capable of defending and justifying their AI use in capstones, portfolios, or thesis projects, demonstrating not only technical fluency but also an internalised sense of academic and professional integrity.

This progression confirms a key message: AI can coexist with academic integrity when its use is grounded in reflective purpose, ethical alignment, and disciplinary growth. The role of educators and institutions is to support that transformation, not by removing the tools, but by building the intellectual and moral scaffolds that help students use them wisely (Long & Magerko, Reference Long and Magerko2020).

By treating AI literacy as a core graduate attribute, universities can ensure students are not only tool users but informed navigators of an evolving digital landscape, prepared to lead with integrity in fields where AI will continue to reshape norms and expectations.

How Might Educators and Staff Model Ethical Use for Students?

Integrity must be visible to be internalised. Educators have a pivotal role in modelling ethical AI use – not only by setting rules but also by demonstrating reflective, responsible behaviour in their own practice (Khan, Reference Khan2024b). When educators openly disclose how they use AI (e.g., to summarise academic texts, scaffold quiz questions, or brainstorm lesson ideas), and also articulate its ethical and technical limitations (such as copyright violations, labour exploitation, amplification of human biases, factual inaccuracies, and misalignment), they model transparency, critical evaluation, ethical navigation, and humility.

For example, institutions could design live demonstration panels where educators compare AI-generated and human-graded student responses to the same prompt. These sessions can help surface how over-reliance on AI might distort fairness, miss contextual nuance, or reinforce hidden biases. By critically dissecting both outputs in real time, educators model not only transparency and evaluative thinking but also their role as ethical co-learners in a shared digital ecosystem. This kind of public reflection helps students understand that integrity isn’t static; rather, it’s continually negotiated in evolving learning environments.

Importantly, educator modelling reinforces a broader culture of integrity within institutions. When teachers embody ethical principles in their own workflows, they signal that academic integrity is not simply a student obligation, but a shared community value.

How Do We Get All Faculty and Staff Skilled Up in the AI Age?

A critical tension in the AI transition is the fluency gap between students and staff. Many students engage rapidly and experimentally with new tools but may lack AI literacy and ethical grounding. Educators, meanwhile, may hesitate due to unfamiliarity, ethical concerns, or lack of training, inadvertently leaving students without needed guidance.

Addressing this divide requires future-resilient professional development. AI literacy training must be embedded in faculty development frameworks, not as an optional add-on but as a strategic imperative. Workshops, peer mentoring, and scenario-based training can support staff in confidently resisting GenAI or incorporating it into pedagogy, while remaining attuned to risks and ethical boundaries.

Equally powerful are cross-role collaborations, where students and educators co-design assignments, AI-integrated rubrics, or ethical use guidelines. These partnerships foster mutual understanding, flatten hierarchies, and create space for shared innovation rooted in academic values.

Educators must also play an active role in shaping institutional AI governance ensuring that the deployment of AI tools is aligned with pedagogical priorities, human rights, and academic standards. If faculty are left out of these conversations, AI adoption risks becoming purely administrative, rather than pedagogically meaningful and ethically grounded.

As we move deeper into the AI era, the educator’s role will not diminish; rather, it will become more complex and more critical. To uphold academic integrity, educators must lead with integrity.