Comparing student and educator perceptions of generative artificial intelligence for teaching, learning and assessment: results from two institutional surveys

The rapid advancement of Generative Artificial Intelligence (GenAI) technologies presents both significant challenges and transformative opportunities which will affect every aspect of society including business and enterprise, healthcare and education (Baldassarre et al., 2023). There is a growing recognition that GenAI will fundamentally alter the nature of work and careers, with shifts in employment patterns and skill requirements already evident, although we are only beginning to understand the nature and magnitude of these changes (Delloitte, 2025; Pratschke, 2024; Vivek et al., 2024; Whelan et al., 2024).

GenAI has been hugely disruptive for Higher Education Institutions (HEIs), perhaps the most significant concern since the COVID-19 pandemic, when traditional approaches to teaching and assessment were interrupted. However, unlike the pandemic, the effects of GenAI are likely to be permanent and change over time as the technology develops and improves. Policy makers and educators within HEIs have had to work quickly to understand how GenAI can be integrated ethically, securely and effectively into the daily operations of HEIs (UNESCO, 2025). It is widely acknowledged that GenAI may enhance efficiencies in administrative tasks, for example by aiding the drafting of meeting agendas and minutes, assisting in responding to email correspondence and other routine tasks, commonplace in academia (Chiu et al., 2023). The integration of GenAI into teaching and learning practices is certain but the level and extent of integration is perhaps more difficult to predict (Schalk Quintanar and Rooney, 2025; TEQSA, 2024). There are many studies that focus on the use of GenAI in teaching and learning activities for specific cohorts of students or within certain academic disciplines (Kovalainen et al., 2025; Memarian and Doleck, 2024; Modran et al., 2023) and experimental research on the application of GenAI for assessment practices, such as comparing human and AI generated feedback (Gupta and Nyamapfene, 2025; Kaliisa et al., 2025; Steiss et al., 2024; Yildiz Durak and Onan, 2025). Similarly, within our own institution we are aware of some case studies of experimentation with GenAI in teaching and assessment activities but again these are somewhat ad hoc and localised to individual modules. It is likely that there will be more rapid developments in this aspect of pedagogy given the volume of research and scholarship activities on this subject with more coherent evidence on student educational outcomes and student experience emerging over time.

As HEIs embark on this journey of GenAI integration, it is critical that educators and students are equipped with the necessary skills and competence to use GenAI in an ethical and safe manner. This will help to optimise use of the technology within the various operations of HEIs and effectively manage risks associated with inappropriate use or the emergence of digital divides and inequalities between or within groups. Indeed, the urgent need to enhance AI literacy, as a key component of digital literacy, in education settings is commonly highlighted by government agencies and experts in the field (AI Advisory Council, 2025; Beckman et al., 2025; Pratschke, 2024). From an institutional viewpoint, the EU AI Act (2024) formalises the responsibility of employers in European countries, including HEIs, to provide AI literacy training to employees. This legislative requirement extends only to staff within HEIs but not to student groups which is instead guided by guidance and policy frameworks such as the UNESCO AI Competency Framework (2024). This provides a roadmap for the integration of AI into education presented as 12 competencies across four domains: Human-centred mindset, ethics of AI, AI techniques and applications and AI system design (UNESCO, 2024). This framework acknowledges the implicit role that AI will occupy in education and helps to prepare students for a future in which AI is inherent in most activities. However, while abstract guidance and legislation are invaluable, there is not only a requirement, but an expectation that they will be supplemented with granular local information informing HEI's decision process. It is to this end where this paper intends to contribute.

GenAI has the potential to have deleterious consequences for the quality and integrity of academic qualifications if integration of this technology into academic practice is not carefully managed. National quality assurance agencies, statutory and regulatory bodies and professional networks have sought to produce guidance and policies to encourage a prudent and structured response. These resources commonly provide the basis for institutional policies or guidelines which are tailored to reflect the needs of the institution. In the Irish context, the National Academic Integrity Network (NAIN), established by Quality and Qualifications Ireland (QQI), has produced a series of guidelines relating to academic integrity, academic misconduct and GenAI (NAIN, 2021, 2023). The AI Advisory Council, established by the Irish government in 2024, published an advice paper relevant to the wider education sector which highlights some key considerations for educators and policy leaders (AI Advisory Council, 2025). Within our own institution, many supports have been introduced for educators and students on the appropriate use of GenAI in teaching, learning and assessment activities. An interim statement, published in March 2024, provided clarity and direction for educators and students whilst the academic integrity policy and associated misconduct procedures were being updated and whilst the institution also developed other educational and support initiatives relating to GenAI. This ensured that educators and students had a clear direction on the permitted use of GenAI in teaching, learning and assessment activities. Many teams and departments within the institution also collaborated in the development of resources and the delivery of educational initiatives to further upskill educators and students on GenAI. These educational materials were also informed by the institutional principles for the use of GenAI, which had been published in June 2024 and were applicable to all university stakeholders, academics, researchers, professional staff and students (UL Academic Integrity Unit, 2024).

It is clear that much work has been done to adapt to the rapid emergence of GenAI nationally and internationally, through the development of legislation, publication of policies and guidelines and research on the subject. The existing body of literature indicates that the pervasive integration of GenAI within higher education is exerting, and will continue to exert, significant influence across the full spectrum of pedagogical practices. These impacts extend from teaching and learning practices to the formulation and enforcement of academic integrity policies, as well as to strategic planning processes at the institutional level. A number of institutional or cross-institutional studies have sought to map current practices and attitudes towards GenAI (Mohammadi et al., 2026; Morari et al., 2025; Nguyen and Goto, 2024; QQI, 2025; Reiter et al., 2025; Roe et al., 2024). These studies commonly focus on a specific dimension of GenAI such as prevalence of use, innovative pedagogical practices, AI literacy, academic integrity and academic misconduct and often focus on either staff or student populations. The aim of our research was to gain insights from both staff and students on a broad range of topics including current Gen AI practices and perceptions in teaching, learning and assessment activities and projected impacts on future career and skills needs. In the continuously evolving Gen AI landscape, these insights on a breadth of topics will be essential to better inform the development of information and educational materials and policies and guidelines to support teaching, learning and assessment, academic integrity and skills development, both within our own organisation but within HEIs nationally and internationally.

This research used a cross-sectional survey design of two groups, registered students and educators. Two online surveys were developed, one circulated to students, and one circulated to educators in an Irish university. An online survey design, using MS Forms, facilitated efficient and cost-effective dissemination. The anonymous and accessible aspects of surveys were the primary reason this study design was selected as it would facilitate rapid and honest insights into the subject matter.

The questions for educators (23 questions) and students (29 questions) were divided into the following broad themes:

1. Use of GenAI

2. Learning and GenAI (student theme); Teaching, learning and assessment (educator theme)

3. Assessment and GenAI

4. Academic integrity and GenAI

5. Upskilling, support, the future and GenAI

These themes were identified following a review of the literature focused on survey-based research on GenAI and teaching, learning and assessment. The questions were also informed by the learning that the authors had gained from the innovations, policies and guidelines that had been introduced within the institution. All the questions were closed-ended and consisted of Likert Scales to determine agreement with specific statements, multiple-choice options, of which one or multiple answers could be selected as appropriate, or questions which provided an opportunity to rank preferences as the response.

Ethics approval for both surveys was obtained from the Faculty of Arts and Humanities (AHSS) (Reference: 2024_09_07_AHSS, University of Limerick) on 16th October 2024. Consent for completion of the survey was provided by participants at the outset and they were also signposted to an information leaflet which provided more detailed information including an outline of study's procedures, the purpose of the research and the study's aims andtheir rights as respondents, including the right to refuse participation. Consent had to be provided prior to proceeding to the start of the survey. Responses were anonymous at the point of data collection to encourage open and honest engagement with the survey questions. Once the survey was completed, it was not possible to undertake the survey again using the same institutional e-mail address.

All students registered at the university, at undergraduate and postgraduate level, were eligible to participate in the survey. This included students who are undertaking a single module of study for example through micro-credentialing. There were approximately 18,000 students registered with the university at the time of the study. There were approximately 1,100 educators working in student-facing roles that were involved with teaching, learning and assessment activities, all of these staff were eligible to participate in the survey. As a number of the survey questions required direct experience or views on the integration of GenAI into teaching or assessment activities, it was important that those with direct student involvement were invited to complete the survey. To prevent individuals external to the university from completing the survey, only those with an institutional email address could log on to the survey.

The online survey was advertised by two electronic posters across campus, one directed at educators and the other at students. A link to the survey was placed on the local staff communication platform. Relevant university social media accounts such as the undergraduate and postgraduate student unions also advertised the survey to registered students. The survey remained open for a 6-week period, with reminders posted periodically.

The responses were initially assembled as two separate spreadsheets, one each for the educator and student responses. These spreadsheets were imported to Stata Version 19 to be merged and then analysed. For the analysis, descriptive statistics were calculated showing the percentage of responses to each question. Furthermore, for questions that were asked to both staff and students, direct comparisons were made between the students and staff responses and differences in responses were tested statistically. In relation to the statistical testing, the p-values arising from Z-tests of differences in proportions are shown where the questions were binary (e.g. usage of a particular AI). Where the responses were categorical (e.g. strongly agree, somewhat agree, somewhat disagree and strongly agree), p-values arising from a Pearson chi-square test of association are shown.

A total of 158 students completed the student survey while 109 staff members completed the educator survey. The average completion time for the educator survey was 9 min 47 s while the student survey was 5 min 53 s. Table 1 presents the percentage of student (column 1) and educator (column 2) respondents in each category. Column 3 contains the p-value of a test of difference between student and educator responses. Table 2 presents the percentage in each response category for questions related to Academic Integrity. Table 3 presents the percentage in each response category for questions related to the potential benefits of GenAI.

The first main finding is the difference between students and educators in whether they used AI. While only 14.6% of students had never used GenAI, just under half of the educator sample (46.4%) had never used GenAI. This large difference was statistically significant. However, among those who had used GenAI, there was no statistically significant difference between students and educators in self-rated confidence levels with GenAI. Educators expressed greater confidence in their usage of GenAI. For example, just 5.1% of educators said they were extremely lacking in confidence as opposed to 11.9% of students. Despite this finding, we could not reject the null hypothesis of no difference in self-rated confidence between educators and students (p = 0.453).

Despite the lack of difference in confidence levels, there was a clear difference between educators and students in their preferred GenAI tool. For ChatGPT and Microsoft (MS) Copilot, this difference was statistically significant (p = 0.028 and p = 0.001 respectively). It should be noted that in the questionnaire, respondents were permitted to name more than one preferred GenAI tool, hence the categories do not add to 100%. Students overwhelmingly stated that ChatGPT was their preferred tool (79.3%) whereas only 64.4% of educators did so. On the other hand, while 34.1% of students mentioned MS Copilot as their preferred tool, 59.3% of educators did so. This difference in usage of Copilot is perhaps not surprising because the MS platform had been automatically installed on university staff computers. Other tools such as Claude, Gemini and DALL-E were much less popular among both educators and students.

In relation to the purpose of usage, 73.3% of students and 72.9% of educators used GenAI for brainstorming, hence there was no statistical difference between educators and students. There were larger differences between educators and students in terms of spellchecking (29.6% of students versus 23.7% of educators) and editing of drafts (37.8% of students versus 33.9% of educators). However, the associated p-values show the differences between educators and students in purpose of usage was not statistically significant.

This section presents an analysis of participant responses concerning the integration of GenAI into teaching, learning, assessment and feedback practices (as presented in Table 1). There were marked differences between students and educators in their opinions regarding the role of GenAI in education. For example, 22.2% of students strongly disagreed that the University should include more GenAI in its teaching, whereas the corresponding percentage of educators was just 5.5%. These differences between educators and students as to whether the University should include more GenAI in teaching were statistically significant (p < 0.001).

When asked about the use of GenAI in formative assessment, responses from students and educators revealed differences in some respects. Among students, 23.4% strongly disagreed with the use of GenAI, contrasting with just 5.5% of educators. A larger proportion of both groups expressed moderate support: 36.1% of students and 47.3% of educators somewhat agreed. Strong support was also evident, though to a lesser extent, with 20.9% of students and 21.8% of educators strongly in favour. These findings suggest that a majority of both educators and students have a positive attitude toward GenAI in formative assessment. However, a sizeable proportion of students do not have an openness to GenAI's potential in non-graded, developmental learning contexts. The p-value from the statistical test (0.001) indicates that there was statistical difference between the opinions of educators and students in relation to formative assessment.

Relative to formative assessment, attitudes toward GenAI in summative assessment showed less divergence between students and educators (Table 1). However, it was still the case that students were more likely to oppose usage of GenAI. For example, 34.8% of students strongly opposed the use of GenAI in this context, compared to 20.9% of educators. While 31.6% of students somewhat agreed, a notably higher 45.5% of educators expressed the same sentiment. Interestingly, strong support was higher among educators (12.7%) than students (8.9%). These results indicate a marked disparity in perceptions, with students demonstrating greater resistance to GenAI in high-stakes assessment, possibly due to concerns about fairness, transparency or academic integrity. Educators, on the other hand, appear more receptive, potentially reflecting a belief in GenAI's capacity to enhance efficiency or objectivity in grading. In this case the p-value from the statistical test (p < 0.001) indicated a statistically significant difference between educators and students in relation to summative assessment.

Regarding the use of GenAI in formative feedback, responses showed a degree of alignment between students and educators. Among students, 30.4% strongly disagreed with GenAI use, closely followed by 24.5% of educators. Moderate agreement was reported by 34.8% of students and 31.8% of educators, while strong agreement was expressed by 13.9% of students and 16.4% of educators. These figures suggest a shared cautious optimism about GenAI's role in formative feedback, with both groups recognising its potential to support learning while maintaining some reservations. Furthermore, statistical testing yielded a p-value of 0.504 which indicated no statistical difference between educators and students in their views on GenAI usage in formative feedback.

In contrast, there is some evidence of divergence in opinion in relation to GenAI use in summative feedback. Nearly half of the student respondents (48.7%) strongly disagreed with its use, compared to 35.5% of educators. Moderate agreement was reported by 22.8% of students and 33.6% of educators, while strong agreement was relatively low in both groups (7.6% of students and 5.5% of educators). This pattern reinforces the earlier finding that students are more sceptical of GenAI in summative contexts, possibly due to concerns about depersonalisation or the perceived inadequacy of AI-generated feedback in high-stakes scenarios. Here, the statistical testing yielded a p-value of 0.090 indicating a statistically significant difference at the 10% level but not the 5%. Thus, there was some weak statistical evidence of a difference between educators and student opinions in relation to GenAI usage in summative feedback.

As presented in Table 1, the majority of students are concerned about breaching academic integrity with their use of GenAI, 34.2% were somewhat concerned and 42.4% were extremely concerned. Educators were overwhelmingly concerned about students breaching academic integrity through their use of GenAI (50% somewhat concerned and 50% extremely concerned). Just 23.4% of students were not concerned about breaching academic integrity. Not a single educator respondent was not concerned about students breaching academic integrity.

Table 2 shows the responses to questions about Academic Integrity that were asked separately to students (Panel A) and educators (Panel B). 61.4% of students say they have used GenAI for academic work. Students were then asked if they were aware of unauthorised usage of GenAI. 26.6% of students said they were unaware. 27.8% said they were unsure and 45.6% said they were aware of unauthorised usage of GenAI. 72.4% of students indicated that they did not intend to use GenAI when it is not permitted, while 17.9% were not yet sure what they would do. Just over a half (55.1%) of students indicated that it was clear to them when GenAI was permitted for assessment. 25.6% of students admit using GenAI when it was not permitted.

In relation to the educator respondents, under a quarter of educators (23.6%) were unsure of UL’S stance on GenAI, which had been issued in March 2024, through a university-wide communication. It is also worth noting that a majority of educator respondents either somewhat agreed (34.5%) or strongly agreed (29.1%) that invigilated exams were the only way to maintain academic integrity.

There appears to be differing importance attached to the likely relevance of GenAI for future careers. From Table 1, one can see that 26.6% of students indicated they did not think GenAI will be important at all, while just 6.4% of educators shared this view. At the other end of the spectrum, over half of educators and one-third of students agree that GenAI will be “Extremely Important” in the future of work. In relation to whether GenAI will change the type of work done in the future, 45.4% and 37.3% of educators somewhat agree or strongly agree respectively, compared with 40.5% and 25.9% of students. These differences were statistically significant.

Table 3 shows responses to questions about skills and the potential benefits of GenAI. The questions asked to students and educators were not directly comparable. Panel A shows the responses of students. While the majority of students agree that GenAI helps them be creative (42.4% somewhat agree and 17.7% strongly agree), 26.6% of students strongly disagree. In relation to GenAI helping students to collaborate, a majority of students disagree (31% strongly disagree and 25.3% somewhat disagree). On the other hand, students are more optimistic about GenAI helping them be more efficient in their learning (38% somewhat agree and 32.3% strongly agree). Lastly, students are nearly evenly split in whether they agree that using GenAI for assessment helps them to be efficient. Broadly speaking, the results show that students have mixed views about the potential benefits of GenAI.

In contrast, Panel B of Table 3 shows that educators seem more optimistic about the benefits of GenAI. A clear majority of educators agree that GenAI will help them be more efficient and innovative in their teaching. Furthermore, a majority also agree that GenAI will help collaborative learning among students. Lastly, a majority of educators felt they had the skills to integrate GenAI into their work.

This survey, the first of its kind within our institution, sought to understand practices and experiences relating to both educators and students use of GenAI. In the year prior to this survey, our institution adopted a pragmatic approach in addressing the training and support needs of all staff and students, through the provision of guidance documentation, short courses and workshops. National guidelines and advisory papers were also published around this time which provided further direction to educators and institutional policy developers (AI Advisory Council, 2025; NAIN, 2023). This survey provides insights as to the efficacy of these multi-pronged interventions and their influence on experiences and perceptions of GenAI for teaching, learning and assessment activities. The results of this survey will inform institutional planning regarding policy development, educational interventions and initiatives to support pedagogical innovations as we seek to understand the impacts of GenAI on higher education.

Similar surveys have been conducted at an institutional or cross-institutional level globally which provides a useful point of comparison and aids interpretation of our findings. Some degree of caution is required in directly comparing survey outcomes for institutions given the many differences between HEIs, including resources, organisational structure, student numbers, strategic aims, culture etc, particularly those outside of the EU, considering the EU AI Act (2024) and the Bologna process, which brings some degree of uniformity to European educational institutions (European Commission, 2025). Perhaps of most interest are the results of a national survey conducted by QQI, which was circulated after our survey (QQI, 2025). It is worth noting that the QQI survey received 1229 staff responses and 1005 learner responses from all Irish further education, higher education and English language education sectors across Ireland. Our institutional survey has therefore a greater per capita response rate and provides insights specific to our institution. Nonetheless there are some elements of both surveys that can be directly compared.

Greater than 80% of students reported use of GenAI in our survey which is a much higher figure than the QQI survey which reported 65% use, ranging from rarely (26%) to “almost every day” (8%) (QQI, 2025). Self-reported use of GenAI is high amongst the student population. Our institutional findings in this regard align with international studies; the Higher Education Institute (UK) indicated that 92% of students reported use of GenAI, a survey of approximately 6000 German university students indicated two-thirds of students were using GenAI and 98% of students of a South African cohort of students declared use (Freeman, 2025; Smit et al., 2025; Von Garrel and Mayer, 2023). Meanwhile two-thirds of students surveyed in an Australian University self-reported use of ChatGPT (Gruenhagen et al., 2024). By contrast, self-reported use by educators is somewhat lower, slightly more than half of respondents. A large scale survey across 20 countries (n = 2021 competed surveys) reported a similar finding with 54% reporting use of once per month for academic purposes (Mohammadi et al., 2026). Other surveys have reported slightly higher use amongst HEI staff, one survey found >75% daily or weekly use (Baig and Yadegaridehkordi, 2025). This variable self-reported use of GenAI amongst staff might be aligned to scepticism and ethical concerns about the use of GenAI described in a number of publications (Chan and Lee, 2023; Roe et al., 2024; Sevnarayan and Potter, 2024).

Whilst our survey did not seek to determine AI literacy, the self-reported confidence level might act as a form of proxy measure for self-perceived literacy. It is quite positive, therefore, that approximately two-thirds of both student and educator respondents indicated a good degree of confidence. By comparison just under half (46%) of staff respondents and a similar proportion of students (69%) in the national QQI survey indicated that they were somewhat to extremely knowledgeable of GenAI (QQI, 2025). Focusing on confidence may be an important avenue to optimise GenAI use. Liu (2025) indicated that prioritising efforts on improving confidence levels and readiness to use AI directly affects the individual's intention to utilise the technology (Liu, 2025). This may be achieved through concerted and meaningful education to support AI literacy in order to foster these behaviours. Indeed, AI literacy has been identified as a priority by many national and international agencies as a means of preparing staff and students for the likely further integration of GenAI into all aspects of our personal and professional lives (AI Advisory Council, 2025; Miao and Holmes, 2024).

Aside from the specifics of literacy and general use, GenAI presents a significant and new threat to the integrity and trustworthiness of assessments methods that have been used for many decades, even centuries, particularly when used in a manner which has not been approved of by the educator (An et al., 2025; Luo, 2024). However, academic dishonesty, or academic misconduct as it is also termed, is not a new phenomenon. The McCabe surveys, a large scale study conducted over a 15 period across US universities, reported that up to 70% of undergraduate students had engaged some form of academic misconduct (McCabe, 2016). A particular strength of our survey was it sought to understand the level of academic dishonesty associated with the unauthorised use of GenAI, both by asking students about their own use and that of peers. While only one-quarter indicated they had used GenAI in an unauthorised manner, many students suspected more widespread use amongst their peers. This finding may have arisen for several reasons: students are unlikely to admit academic misconduct (in this case unauthorised GenAI use) themselves; unauthorised use is truly lower than perceived or students perceive a greater level of unauthorised use amongst their peers than actually exists. A previous studiy in our own institution also found that students tend to perceive their peers incur academic integrity breaches more than they do (Risquez et al., 2013). Our study findings in this regard would appear to align with that of Nguyen and Goto (2024) which estimated using an indirect questioning technique that approximately 24% of a Vietnamese undergraduate cohort (n = 924) had engaged in academic misconduct through ChatGPT (Nguyen and Goto, 2024) and also the results of a study of 498 students in a large European University which reported a relatively low incidence (21%) of cheating behaviour associated with GenAI (Reiter et al., 2025).

The educators in our survey are clearly concerned about the threat presented to academic integrity by GenAI, all respondents were either somewhat or extremely concerned. Acknowledging the fact that there is no silver bullet that mitigates against the threat to academic integrity, experts in the field have identified various approaches to integrating GenAI into assessment practices, so-called discursive changes (Corbin et al., 2025). Such approaches which require that students honestly declare the level of GenAI use in assignments, recognise the efficiencies and educational benefits associated with the use of the technology. Indeed, within our institution, we have incorporated the AI Assessment Scale into the educational resources targeted at educators and students, as a means of providing a simple and structured framework that might be easily adopted by the entire campus community (Perkins et al., 2024). We have anecdotal evidence of application of the AI Assessment Scale in our institution but do not have formal data on its use. Similarly, the university's stance on GenAI use has been communicated to all staff and students through course materials and websites. The staff population reported a greater awareness of awareness of the university's stance on GenAI (>75%) in comparison with 37% of staff in the national survey indicated that they were unaware of GenAI or related policies or guidance within their home institutions (QQI, 2025). While this finding provides positive insights into our institutional efforts to ensure clear and consistent messaging, it is apparent there is some work to be done in this area. The primary issue may not be related to the existence of policies or guidelines, rather awareness and implementation amongst key stakeholders. Several authors have similarly highlighted an urgent need for policies or guidelines that are accepted and applied by all stakeholders to allay the anxieties of educators and students in relation to academic integrity (Ellison et al., 2026; Yusuf et al., 2024).

The potential role of GenAI in formative and summative assessment could revolutionise the educational experience for both educators and students. Overall, the data reveal a consistent trend: both students and educators are more accepting of GenAI in formative contexts than in summative ones. However, staff tend to be more supportive of GenAI integration across both domains. The no-stakes or low-stakes nature of formative assessment may make the use of GenAI more acceptable for both populations. Furthermore, the alignment of views on formative assessments suggests a shared understanding of GenAI as a supplementary tool for learning, while the divergence in summative contexts highlights differing levels of trust in GenAI's role in evaluative processes. These insights underscore the importance of context-specific implementation strategies and the need for transparent communication about the capabilities and limitations of AI in education.

Educators would appear to be more in favour of the use of GenAI for summative assessment while students are more in favour of its application in formative assessment. Overall, the attitude towards the integration of GenAI for marking and assessment amongst students can be described as somewhat guarded, which is broadly in keeping with existing literature on this subject (Roe et al., 2024). The moderately less sceptical views of educators towards the use of GenAI for summative assessment differs to similar studies on this subject (Lee et al., 2024). However, it should be noted that our study did not seek to understand the nuanced views on the level of educator involvement to support the role of the AI in the summative assessment process. This may indeed be crucial to the interpretation of this finding, as qualitative studies on this subject have indicated a more considered view, one that welcomes the potential for reduced administrative burden of assessment but that wishes to retain the educator as the final decision-maker in an assessment outcome (van den Berg and Papadopoulos, 2024). Much has been made of digital natives as enthusiastic users of GenAI (Chan and Lee, 2023) with a large gap hypothesised between educators and students. This is not evidenced by the results of our survey relating to potential applications to assessment in particular, whereby students would appear to have a somewhat cautious approach to the integration of GenAI. There appears to be value placed by students on the role of educators in assessment and feedback processes as evidenced by this general reluctance to entirely embrace GenAI. The apparently contradictory attitudes of students towards the use of GenAI, as part of the assessment and feedback process, by those who are responsible for marking and feedback when some students themselves use GenAI to assist with assessment, that is “do as I say, not as I do”, can be attributed to the high value and trust placed on educators when it comes to marking and feedback (Corbin et al., 2025). This finding is echoed in a qualitative research study with students (n = 12) in a Dutch university, who acknowledged the possible role of AI in the marking and feedback process but highlighted the importance of a human to validate and improve the quality of the feedback offered by the AI (van den Berg and Papadopoulos, 2024). Indeed, the continued need for human involvement in marking and feedback, even if to supplement an AI-led process is a conclusion of many authors (Bates et al., 2020; Latheron et al., 2025; Roe et al., 2024).

When seeking to integrate GenAI into assessment and feedback practices, it is essential to distinguish between two discrete types of intention and purpose of the assessment and feedback process. There needs to be a recognition that the developing learner has a specific set of cognitive and emotional needs that demands that they are recognised as an individual and requiring recognitive feedback, recognising the individual and which can sometimes necessitate bi-directional communication between student and educator. By contrast, extra-recognitive feedback is unidirectional in nature, and although may be capable of having a personalised message, is somewhat depersonalised from an affective viewpoint (Corbin et al., 2025). This type of feedback may be the aspect that concerns students, given the educator seems somewhat removed from the process. From an institutional perspective, integrating GenAI into assessment and feedback processes is a delicate and potentially problematic proposal, not merely from a data security viewpoint but from an ethical and acceptability perspective, for educators and student. However, in an era of increasing class sizes and pressures on educators to deliver on large teaching loads that are associated with substantial administrative burdens, the integration of GenAI into assessment and feedback may be inevitable and necessary. We propose, based on our findings in this survey, that extensive consultation is required with both students and educators before GenAI is widely integrated into assessment processes.

While there was a certain level of divergence and nuance on issues of assessment and feedback, both groups in our survey acknowledged that GenAI would likely impact their career in terms of its importance and impact on ways of working although educators perceived a greater impact than students. Indeed, students internationally appear to be aware of the potential impact of this technology on the future of their work (Thomson et al., 2024) and this reflects the predictions from industry leaders and those who have investigated such impacts. Educators, have perhaps greater awareness of GenAI's obvious impacts, given that they themselves are operating within a workplace, impact is not an abstract concept to this group. This convergence of opinion illustrates the unanimity of the importance of the issue of GenAI's transformative potential on HEI's and beyond further highlighting the importance of this study and others like it, in informing decision making and institutional policy going forward.

Surveys are widely recognised to have several limitations which must be acknowledged. Completion of the survey was voluntary and it is possible that individuals with a strong interest or opinions on GenAI and education opted to complete the survey, which may have skewed data (Evans and Mathur, 2005). While it was clearly communicated to staff members that only those with a student-facing teaching, learning or assessment role should complete the survey, there was no mechanism available to prevent staff members who did not meet this criterion from participating. The response rate the survey was relatively low, slightly less than 1% of the student population and less than 10% of the educator population. It is therefore not possible to make sound inferences as to the extent to which this survey represents the views of students and educators within the university (Andrade, 2020). To optimise completion of the survey, all questions were closed-ended and did not seek to examine any subject in significant depth. This undoubtedly detracts from the nuanced insights that could be gained on the subject matter. However, online surveys are also a user-friendly and cost-effective method of gathering information from a cross-section of the campus community, particularly when a large number of staff and students engage in a hybrid manner with university activities. Finally, it is important to acknowledge that practices and perceptions relating to AI are rapidly changing and evolving as the technology becomes increasingly embedded in every aspect of day-to-day life. These surveys were conducted in late 2024, it is highly likely that the results of this survey would vary considerably each passing academic year.

From a practical viewpoint, the findings of our research highlight a range of issues for those involved with the strategic implementation of GenAI in HEIs and for educators:

1. Importance of establishing an account of baseline use and attitudes towards GenAI to bridge the gap between policy and practice

It is imperative that those involved in strategy and policy roles within HEIs have a clear sense of use, experiences and attitudes towards GenAI by key stakeholders within their organisation. The findings of our research aligned within published research (e.g. relating to level of GenAI use and concerns around academic integrity). However, several findings differed considerably or indeed were contradictory to existing literature. This indicates that localised educational interventions, policy or guidelines may have a significant influence on stakeholder viewpoints. As such, it becomes clear that valuable policy work still requires explicit accounts of praxis to facilitate implementation. More in depth and data driven accounts of both stakeholder attitudes and detailed descriptions of GenAI use is essential to bridge the gap between policy development and concrete adoption.

1. AI literacy as a mediator for innovative teaching, learning and assessment practices

There is an apparent curiosity amongst both students and educators for possible applications of GenAI for teaching, learning and assessment processes. However, GenAI use remains conservative and underpinned by uncertainty. The contrast between high student usage (>80%) and moderate educator uptake (∼53%), combined with educators' openness to using GenAI and students' reservations around AI-mediated summative feedback, signals an important shift in how teaching and assessment should be conducted. The core implication is not whether GenAI is permitted but how teaching and assessment practices can be structured in such a way to foster learners' reflection, critical thinking and innovation as they navigate the complexities of an AI infused reality. The outcome is a learning ecosystem where students use GenAI responsibly while being assessed on the processes of learning and their higher order thinking skills. This highlights a need for a significantly greater provision of AI literacy for all stakeholders within HEI's. While the mandate within the EU AI Act (2024) will be helpful to ensuring that this takes place, the discrepancies visible in our study, show that HEI's not only require institutionally specific approaches to AI literacy, but arguably cohort specific programs within the institutions themselves. Tailored programs for educators and students will hopefully lessen the discrepancies, but consequentially, may make facilitation of GenAI's embedding in teaching, learning and assessment processes more coherent and acceptable.

GenAI is having a significant impact on society, changing the way in which people seek out information, communicate with one another and conduct daily business. Education, particularly teaching and assessment practices, are likely to undergo seismic changes in the coming years as educators and students begin to innovate and adapt to the changes wrought by this technology. There remains limited confidence in working with the technology and concern as to its appropriate or permitted use in academic work. This highlights the need for educational institutions, professional regulators, quality assurance agencies and governmental departments to provide suitable information, guidance and resources to further assist educators and students. There is an urgent need for a systematic and ordered approach to GenAI literacy and frameworks for supporting innovations in teaching, learning and assessment, on an individual institutional level and at a national level. It must also be acknowledged that there is unlikely to be a homogenous approach to GenAI integration across all academic disciplines, this is likely to become apparent over time and will also be incumbent on educators to understand the shifting professional or disciplinary context into which graduates are emerging.

Author 1: Conceptualisation, Project Administration; Investigation, Data Curation, Formal Analysis, Writing – original draft, Writing – review and editing; Author 2: Data curation, Investigation, Formal analysis, Writing – original draft, Writing – review and editing; Author 3:  Data Curation, Formal Analysis, Writing – original draft, Writing – review and editing; Author 4: Writing – review and editing; Author 5: Writing – review and editing; Author 6: Writing – review and editing; Author 7: Writing – review and editing.

GenAI has not been used in any part of the production or editing of this paper.