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Students are using AI for mental health: what should universities do?

by Eleni Meletiadou

This blog draws on cross-national research conducted within the Digital Mental Health for Young People (YouthDMH) COST Action CA23153, an EU-funded European research network.

Introduction

Students experiencing anxiety do not always approach university counselling services first. Some turn to digital self-assessment, mental-health apps or conversational artificial intelligence (AI). These tools may feel private and accessible, but students may struggle to judge their reliability or recognise when professional help is needed. Research on young people’s online mental-health help-seeking highlights both the opportunities and limitations of digital support.

This blog presents findings from a cross-national study of 220 students in the UK, Slovakia and Slovenia. Conducted within the YouthDMH COST Action. The study examined reported anxiety, the use of AI and other digital mental-health technologies, perceived usefulness, intentions to reuse these tools and concerns about potential harm. As a Working Group member representing the UK, I am particularly interested in how digital technologies can support young people’s mental health and help-seeking while addressing safety, privacy and unequal access.

The findings raise an important question for higher education: how can universities respond responsibly when students are already using digital tools to manage mental-health difficulties? Rather than simply encouraging or discouraging their use, institutions need to understand students’ experiences and ensure that digital resources complement, rather than replace, appropriate human support.

What my research reveals

Reported anxiety was highest among students in the UK, lower among those in Slovakia and lowest among those in Slovenia. Around half of participants reported using AI or digital mental-health technologies. General-purpose conversational AI, particularly ChatGPT, was used more frequently than dedicated mental-health tools. Perceived usefulness was the clearest factor associated with intentions to use AI again to manage mental-health difficulties. Participants also raised concerns about data protection, dependence, and potential negative effects.

These findings suggest that university policies should consider the tools students actually use, not only those institutions formally recommend. However, the survey does not establish why students choose particular tools. Perceived usefulness and intentions to reuse do not demonstrate that anxiety has improved, that advice is accurate, or that appropriate help has been received. Similarly, differences in reported anxiety across countries do not, by themselves, explain the reasons for those differences or establish their statistical significance.

What previous research adds

Previous research has documented cross-national differences in university students’ mental health, including a study of students in nine countries by Ochnik et al (2021). More recently, a rapid systematic review by Nyakhar and Wang (2025) examined the effectiveness of AI chatbots for mental health and wellbeing among college students. Their review identified promising findings but also limitations in the evidence, highlighting the need for more rigorous research.

My study brings reported anxiety, digital-tool use, perceived usefulness, intentions to reuse, and concerns about harm into one analysis across three national contexts. This offers a more specific basis for institutional discussion than treating digital support as a single category. Evidence about structured mental-health interventions cannot automatically be taken as evidence that general-purpose chatbots reduce anxiety, nor can findings about AI alone explain how students use the wider range of digital resources.

What should universities do?

1. Build digital mental-health literacy into student support. Students need practical guidance on evaluating information, recognising technological limitations, protecting sensitive data and understanding that digital tools cannot reliably diagnose anxiety. This guidance could be integrated into induction, personal tutoring, digital-skills provision and wellbeing support. The aim should be informed use, not simply telling students to avoid technology.

2. Connect digital self-help to human support. Counselling, wellbeing services, primary care and urgent support should be easy to find. Apps, online questionnaires and chatbot interfaces should communicate their limitations clearly and provide understandable next steps. Digital resources should complement, not replace, accessible and adequately resourced professional services.

3. Assess tools before recommending them. Universities should review privacy, reliability, accessibility and evidence of effectiveness before endorsing digital mental-health resources. They should also explain why a tool is recommended and how student information may be processed. The World Health Organization’s guidance on AI ethics and governance for health offers relevant principles for safety, transparency and accountability.

4. Involve students in service design. Universities should ask students which tools they use, what they find useful and what prevents them from seeking help. Consultation should include students with different backgrounds, language needs, disabilities and levels of digital confidence. A single app or support pathway will not meet everyone’s needs.

5. Measure outcomes, not just uptake. Downloads, satisfaction and intentions to reuse are insufficient measures of success. Institutions should evaluate whether students can identify reliable information, access appropriate help and navigate services safely. Where anxiety reduction is the intended outcome, suitable measures and research designs are needed. Universities should also examine whether referral pathways work in practice and whether particular groups encounter additional barriers.

From findings to institutional responsibility

The value of this study lies in connecting reported anxiety with digital mental-health tool use, the perceived usefulness of AI and concerns about risk across three national contexts. It moves the discussion beyond a simple choice between embracing and rejecting AI. Universities need to understand the digital landscape students are already navigating and respond with clear guidance, appropriate safeguards, accessible human support and meaningful evaluation. This requires collaboration between academic staff, student services, digital-learning teams and institutional leaders.

Institutions should neither dismiss students’ use of digital tools nor endorse them uncritically. The priority is to help students make informed decisions, recognise the limitations of digital self-help and reach appropriate support when they need it. Responsible digital inclusion means ensuring that technological accessibility is accompanied by reliable information, human care and institutional accountability.

Dr Eleni Meletiadou is Strategic Lead – Academic Development, Inclusion, Research and Innovation at the Centre for Excellence in Learning and Teaching (CELT), University of East London. She is a Principal Fellow of Advance HE (PFHEA), a National Teaching Fellow (NTF) and a UEL EDI Honorary Fellow. Her research focuses on inclusive and intercultural education, digital and AI-enhanced learning and assessment, equity and social justice in higher education, and student wellbeing. Her work explores how educational innovation can advance inclusion, digital literacy and sustainable learning across diverse educational contexts. She is a Working Group Member in the Digital Mental Health for Young People (YouthDMH) COST Action CA23153, contributing to European research and collaboration on young people’s digital mental health.


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Exploring notions of ‘good’ academic leadership in challenging times

by Alan Floyd

Due to the nature of academic work it is accepted that leaders cannot be effective without the support of their departmental colleagues. Consequently, academic leadership is seen as more of a collective responsibility. Arguably, ‘distributing’ and sharing leadership is even more important in universities than in other organisations as academics are well educated, largely autonomous, and trained to be highly critical, so are more likely to oppose and challenge traditional leadership models and behaviours and may need a subtler form of leadership than other occupational groups. This perceived shift in leadership power, moving from people in formal positions to the whole academic body, is important for universities as it has been shown that the leadership activity of academics outside formal leadership roles can be very influential in effecting organizational change. Such a power shift may also allow academics to discuss and decide on leadership issues in a more collegial manner, a practice more in line with the shared value systems of the academy. But what are academics’ expressed notions of ‘good’ leadership within this context?  This blog explores these issues by drawing on data from research that has explored more flexible ‘distributed’ and ‘collaborative’ models of leadership, crucially focusing on data from both leaders and the led (see Floyd, 2019; Floyd and Fung, 2017; Floyd and Fung, 2018).

From this work, it appears that academics construct ‘good’ leadership predominantly in terms of understanding and supporting others, empathy, the ability both to act with integrity and as a role model, and the willingness to engage in genuine dialogue.  Academics apparently want to see in their leaders all of these personal qualities and knowledge and understanding of the whole ecosystem of higher education and the ability to make tough decisions to make that successful when needed.

The data suggest that academics understand the need for strong academic leadership in the current higher education landscape and there is a lot of empathy for the complexities of leadership practice in these turbulent times. In addition, academics are clear that individuals cannot just ‘plough their own furrow’ without considering the overall needs of the department or institution overall. Thus, academic leadership was seen as being a collective act (Bolden et al, 2009). Shared understandings of good academic leadership included words like ‘respect’, ‘feeling valued’, ‘fairness’, ‘being realistic’, and being ‘open’ and ‘communicative’. In relation to distributed leadership practice then, it seems that academics are happy to work with leaders in achieving shared institutional goals as long as they perceive the decisions taken to be in the best interests of the group, that all people involved (those whose roles are teaching-focused as well as those who are research-focused) are treated fairly and with respect, and that leaders show effective communicate skills and engage in genuine dialogue with academics rather than just transmitting information.

In their personal accounts about the characteristics of ‘good’ academic leadership, some leaders stress the importance of having the right set of skills, while others emphasize a set of underpinning values. There is some difference in emphasis between those whose roles are predominantly focused on research and those whose main focus is student education, but interviewees typically construct a sense of needing to respect both research and teaching as vital strands of academic practice. Most also see academic leadership as necessarily negotiating the sometimes-differing needs of the institution itself and the individuals being led.

There were many similarities in our findings between the two groups. For example, from the leaders’ point of view, good academic leadership was characterised by holding ‘shared underpinning values’, ‘good listening and communication skills’, being ‘understanding’, ‘supportive’, and ‘even handed’, and ‘human’ in their relationships with academic staff.  There also appeared to be a shared understanding from both groups of the need and difficulty in ‘balancing’ institutional and individual needs.

One key theme that emerged from our data was the perceived need for appropriate career support from leaders in relation to academics’ chosen career paths. This finding suggests that the focus of development training and support activities for academics who take on leadership roles may need to be widened from traditional activities (for example, linked to managing conflict and finance) to include more discussions on individual staff development needs. Such a finding reflects the move towards more portfolio based careers for academics, with career development responsibility seemingly shifting from the institution to the individual (Floyd, 2012), and an accompanying shift in associated developmental needs for academic leaders (Floyd, 2016). More fundamentally, it also suggests the need for academic work, including research and education, to be seen as a scholarly whole (Fung, 2016), and for university leadership to be seen as a special form of academic endeavour directed at strengthening the synergies between these different areas – for the good of both the individuals themselves and their institutions. The tensions between what is deemed good for the individuals (both leaders and those who are led) and what is good for the institution lie at the heart of the challenge, and our data suggest that all parties appreciate explicit discussion about these tensions, so that shared solutions and indeed shared values and goals can be developed.

Acknowledgments

The research on which this blog is based was funded by the Leadership Foundation for Higher Education and undertaken with Professor Dilly Fung. I gratefully acknowledge their support. Interpretations and errors remain my own.

SRHE member Alan Floyd is Professor of Education at the University of Reading where he is also the EdD Programme Director.

References

Bolden, R, Petrov, G and Gosling, J (2009) ‘Distributed leadership in higher education: rhetoric and reality’ Educational Management, Administration and Leadership, 37(2): 257-277

Floyd, A (2012) ”Turning points’: the personal and professional circumstances that lead academics to become middle managers’ Educational Management, Administration and Leadership, 40(2): 272-284

Floyd, A (2016) ‘Supporting academic middle managers in higher education: do we care?’ Higher Education Policy, 29(2): 167-183

Floyd, A (2019) ‘Investigating the PDR process in a UK university: continuing professional development or performativity?’ Professional Development in Education, 1-15

Floyd, A, and Fung, D (2017) ‘Focusing the kaleidoscope: exploring distributed leadership in an English university’ Studies in Higher Education, 42(8): 1488-1503

Floyd, A, and Fung, D (2018) ‘Stories of leading and being led: developing collaborative relationships in an English research-intensive university’ in Gornall, L, Thomas, B and Sweetman, L (eds), Exploring consensual leadership in higher education London: Bloomsbury Academic

Fung, D (2016) ‘Strength-based scholarship and good education: the scholarship circle’ Innovations in Education and Teaching International 54(2): 101-110