of university of leicester called for A.I. Evidence to the Education Committee of the UK Parliament found that voluntary workshops, events and centrally provided guidance were not reaching enough students and staff to build deeper competencies, so literacy needs to be embedded across the higher education curriculum.
Written evidence submitted by Professor Xue Zhou, Professor of Business Education AI and Head of AI at Leicester Business School, and Dr Sarah Moze, Digital and Learning Innovation Manager at the University of Leicester, sets out a proposed 4P framework covering policy, people, pedagogy and platforms.
The recommendations build on two years of AI education activities at the University, including policies, staff workshops, student resources, lecture programs, interdisciplinary events, and a 240-member AI and Higher Education Community of Practice.
However, university participation data shows a gap between interest and continued engagement. Attendance for all sessions at the February 2026 AI and Robotics Symposium ranged from 10% to 38%, while attendance for the nine staff AI workshops held centrally in 2025/26 was 54%.
The University of Leicester is currently piloting the 4P approach within its Faculty of Management with the aim of informing its implementation across the wider institution. An asynchronous AI course for students is also being conducted on a trial basis, ahead of university-wide implementation in the 2026/27 academic year.
Voluntary AI programs struggle to convert interest into participation
More than 300 people have registered for the AI and Robotics symposium, which was co-hosted with the University of Birmingham and was attended by nearly 200 students and staff both online and in-person, according to a parliamentary filing.
The event included robotics demonstrations, virtual reality simulations, applied AI workshops, panel discussions, and sessions focused on ethics. Those who attended rated this event with an average satisfaction score of 4.89 out of 5, with all respondents saying they would recommend the event.
Other initiatives are also gaining participation. Between February 2025 and March 2026, nine Teach with Tech sessions recorded 182 participants, and after the series began in April 2025, seven online lectures on AI and higher education attracted 474 participants.
Nevertheless, the evidence argues that optional provisions have limited scope, particularly where there is a need to attend sessions in person or where more hands-on involvement is required.
Possible barriers identified by universities include students’ prioritization of paid work, a desire for shorter and more flexible learning formats, concerns about the environmental and ethical impact of AI, and the perception that AI skills will only be relevant later in a student’s career.
For staff, the proposal points to workload, job insecurity and low morale as factors that reduce participation in non-compulsory development. Generally, short online lectures generate more interest than longer hands-on workshops.
University surveys also show growing skepticism about AI’s contribution to learning. The annual survey received 244 responses in 2024/25 and 144 responses in 2025/26.
Students’ average rating for the accuracy of AI-generated academic materials decreased from 2.898 to 2.719 out of 5. Ratings of the impact of AI on academic development decreased from 3.398 to 2.790, and perceptions of the importance of AI skills in future careers decreased from 3.574 to 2.962.
Some students equate AI’s capabilities with the ability to use chatbots, and without deeper skills in verification, evaluation, judgment, and ethical reasoning, they risk becoming overconfident, the submission said.
“AI literacy should be understood as a critical, ethical, and situational competency, rather than simply being familiar with AI tools,” Chou said in a LinkedIn post.
Leicester moves from AI events to 4P framework
The model proposed by the University of Leicester combines organizational governance, human capabilities, curriculum design, and controlled technology implementation.
Under the policy, the university will operate a policy on generative artificial intelligence in learning, teaching and assessment from September 2024. This policy is reviewed annually and is supported by a traffic light system that provides guidance to students and staff on the permitted use of AI in assessment.
The university is also finalizing guidance covering AI in research, including the use of AI in study design, data analysis, and funding applications.
The talent element prioritizes confidence, judgment, and reflective practice, not just technical proficiency. The training is intended to help students and staff decide when to use AI, how to evaluate its output, and when it is inappropriate to use AI.
The College of Business pilot will treat AI as part of the curriculum under pedagogy, rather than as an optional activity. The approach includes understanding the concept of AI, applying AI in specific fields, critically evaluating and creating with AI, and the ethics of AI.
Students are expected to develop proficiency in at least two AI tools relevant to their subject matter and future career path.
Platform elements take a selective approach rather than increasing the number of tools available. Evidence identifies Microsoft Copilot, Blackboard AI Design Assistant, Microsoft Excel, and Sage as workplace-related and education-specific technologies being considered or used.
The submission argues that platform decisions should follow learning objectives and take into account data security, copyright, employability and the risk of overwhelming students and staff with unnecessary tools.
British and Chinese research links learner control and self-reflection
Another paper in which Chou was involved added to research evidence showing the university’s focus on reflexive uses of AI.
Published online higher education research June 11, 2026 The agency gap: Recognizing human AI agency, reflection, and generative AI learning in UK- and China-based higher education contexts. We investigated the responses of 309 university students.
The authors are Aniekan Essien and Marios Kremantzis from the University of Bristol Business School, Zhou from the University of Leicester, and Da Teng from Beijing University of Chemical Technology.
The study analyzed the responses of 145 students studying in the UK and 164 students studying in China. Data was collected between September and December 2025 from undergraduate and graduate students aged 18 and older who reported using AI in their coursework during the period.
In both samples, students who reported greater control over their AI-assisted learning tended to report stronger reflective engagement. In the UK and Chinese samples, reflection was positively associated with self-reported critical thinking.
The indirect relationship between perceived human and AI agency and self-reported critical thinking through self-reflection was statistically significant for both groups.
Professor Chou said: “This suggests that simply interacting with AI is not enough; students need opportunities to reflect on their interactions, evaluate their outcomes, and consider how AI impacts their thinking and learning processes.”
This study defines perceived human and AI agency through reported student initiatives, monitoring of AI output, and control over final learning decisions. It does not objectively measure whether a student’s critical thinking has improved or track actual prompting, validation, or correction behavior.
Differences also emerged between samples. Among UK-based respondents, self-reflection was associated with stronger self-reported academic self-concept, but the relationship was not statistically significant for the China-based group.
The researchers caution that this does not establish any identified differences between the two higher education contexts. Academic self-concept was not measured similarly across samples, and comparisons between groups did not meet the required threshold for statistical significance.
In the Chinese sample, AI literacy slightly strengthened the relationship between agency and reflection. No comparable moderating effects were found in the UK or combined models.
This study is based on a one-time self-report survey and cannot prove that maintaining control causes students to reflect more deeply or that reflection enhances critical thinking. Additionally, the sample was not nationally representative, and the study did not directly measure culture, organizational AI policies, discipline, tool availability, educational background, etc.
Next steps for the University of Leicester include the introduction of an asynchronous student AI course in 2026/27 and continued testing of the 4P model in the Faculty of Management. The parliamentary evidence recommends that universities embed AI competency development into their core programs and assessment structures, rather than relying primarily on students and staff choosing to take additional training.
