Generative artificial intelligence (GenAI) is increasingly influencing university teaching, yet research on how mathematics faculty perceive and adopt these tools remains limited. This study investigates four key areas related to GenAI use in higher education: faculty AI literacy, intelligent technological pedagogical content knowledge (TPACK), perceived trust in GenAI outputs, and overall acceptance of GenAI in mathematics instruction. An online survey was administered to mathematics faculty in Saudi universities. The results show that ChatGPT is the most widely used GenAI tool among participants, indicating its central role in current instructional practices. Findings also reveal generally positive attitudes toward GenAI, alongside cautious levels of trust, particularly regarding the accuracy of AI-generated mathematical solutions. Acceptance of GenAI was found to be closely associated with higher levels of AI literacy, stronger intelligent TPACK, and greater perceived trust. Overall, the study provides insight into faculty readiness for GenAI integration, highlighting both opportunities and ongoing challenges. This exploratory research contributes to the growing literature on GenAI adoption in higher education and offers guidance for professional development initiatives aimed at supporting ethical, effective, and pedagogically informed use of GenAI in university mathematics instruction
This case study examines changes introduced in an undergraduate statistics course following the release of ChatGPT in November 2022. Our primary aim was to support students in making informed and ethical decisions about their personal use of generative AI (GenAI). Survey data collected from students and teaching staff indicate that both groups are more positive about the ethical and practical implications of GenAI compared to peers within the School of Mathematics and Statistics. For students, greater engagement with GenAI appears linked to reduced anxiety and improved confidence in learning statistics, which may contribute to observed reductions in withdrawal rates. Teaching staff reported increased understanding of GenAI’s potential and confidence in their role in guiding students toward its responsible use as a learning aid. These findings highlight the ongoing shifts driven by GenAI and the impact of specific actions taken to support students and staff to incorporate GenAI into students’ learning strategies
This article is based on a keynote presentation delivered by the corresponding author at CETL-MSOR in September 2025 at the University of Liverpool. The article is co-authored with two student partners. It explores the pedagogy of students as partners and how this can be used to develop scholarship of teaching and learning in mathematics. Examples are provided of projects involving the authors on assessment and feedback literacy and the development of scholarly informed activities for abstract algebra teaching.
In today’s fast-paced academic environment, Generative AI (GenAI) offers a powerful opportunity for STEM educators to work smarter – not harder. This article explores how GenAI can streamline the entire assessment lifecycle: from designing complex, discipline-specific assessments and creating detailed marking rubrics to automating feedback and using transcription tools like Whisper to deliver rich, personalised responses efficiently. Drawing on hands-on experience developing curriculum and assessments for a Data Scientist Degree Apprenticeship at the University of Nottingham, we’ll discuss examples that show how GenAI can uphold academic rigour while significantly reducing time spent on repetitive, manual tasks. Rather than replacing academic judgment, these tools enhance it—freeing up time to focus on teaching, innovation, and student support.
One of the goals of an undergraduate degree in mathematics is to transform students’ perceptions of mathematics from calculations with the rote application of formula to the reflective, creative problem-solving that is highly valued in academia and other professions. This can be achieved by incorporating authentic mathematical activities (i.e. the kind of tasks a maths graduate can expect in the workplace) into the design and delivery of undergraduate programmes. The Middlesex maths team have implemented a variety of novel teaching and learning methods into their specialist maths provision to achieve this aim. Our approach includes the use of generative artificial intelligence; extended, vague, problem-solving assignments; student choice in assessment; and reflective components. In this paper we discuss the implementation, benefits, and challenges of these authentic mathematical activities, focusing on their effect on students’ perceptions of mathematics during their studies. We use questionnaires to determine how students’ perceptions of mathematics change while doing these activities and their attitudes to the activities themselves.
The ‘Hub’ at the School of Mathematics and Statistics at the University of Glasgow is a support help room for level 1 and 2 students, staffed by PGT students, PGRs, and course lecturers. Due to large cohort sizes, Glasgow has moved to a ‘team teaching’ model for lower-level courses in which each course has at least two lecturers. Given this, all office hours for level 1 and 2 classes take place within the Hub. Given the size of the pre-honours cohort and the large number of staff members of whom they can ask questions, it is difficult for any one staff member to glean an accurate picture of the areas in which students are struggling. In the current academic year, we asked Hub staffers to record the student number, course code, and question topic for each query asked of them in the Hub. Attendance data were recorded on a printed register by the staffer and later digitised by the investigator. These data were manually aggregated into a database and obvious errors corrected manually. From the details of approximately 1000 student queries, we seek trends on the impact of engagement on final grade, and to identify gaps in students’ knowledge.
Creating step-by-step maths solutions takes significant time and effort. Starting with a ChatGPT-generated draft and proceeding to carefully review and improve it can lead to significant time savings. In this case study, solution documents were created for two past exam papers in a second-year undergraduate maths module. Using a ChatGPT-generated draft as a starting point led to a total creation time of 2 hours and 36 minutes, compared to 4 hours and 31 minutes without the assistance of ChatGPT. This article explains the procedure for obtaining the ChatGPT draft, provides the background for the study, and presents the findings. It highlights key strengths of using ChatGPT for this purpose, including its speed, accuracy and quality of explanation. Limitations are also discussed, such as the risk of calculation errors, incorrect workings or over complicated answers.
The ability to disseminate and communicate densely mathematical and technical material to a non-technical audience and coworkers is a key employability skill for mathematics graduates. As educators it is important that we consider how to bridge this gap and how we can embed these skills into already tightly packed programmes. At Middlesex University we have long believed in embedding communication skills in our undergraduate mathematics programmes to empower our students from diverse backgrounds. Importantly, while these students are with us, we also present them with the opportunity to work as mathematical ambassadors and apply these skills in-situ during outreach and public engagement events. These events include SMASHFest, Big Bang, Thorpe Park, World Skills, Teen Tech, and MDX STEMFest. This increases their confidence and knowledge of mathematical topics further while enhancing their employability, communication skills, and social capital.
The module ‘Introduction to Study and Research in Mathematics’ is a credit-bearing unit of teaching designed particularly with the aim of supporting students in the transition from school to studying mathematics at undergraduate level in the UK. This case study discusses how the design of the module was impacted by consideration of the affective domain, aiming to build both students’ understanding of and interest in mathematics as an academic discipline and their confidence in tackling mathematics questions they do not initially know how to answer.
It is well documented in the literature that students entering their first year of university struggle with adapting to the new teaching style and environment. This is particularly evident in the literature among students enrolled in STEM courses at university, including those enrolled in engineering courses. One of the primary concerns around students entering engineering courses in university is their level of mathematics and its subsequent effect on their learning. The issues identified in the literature focus on students in their first year of university but this neglects students in later years who may also experience issues. Therefore, in this paper, we investigate, through survey responses, if issues which cause students difficulty are present for students beyond their first year of university. Specifically, we report factors that first, second- and third-year undergraduate engineering students at an Irish university have identified as causing them difficulty when studying mathematics. Moreover, we investigate what, if any, impact these issues may have on students' perception of mathematics and their stress levels due to mathematics.
This paper explores the quantitative training needs of Postgraduate Researchers (PGRs) and university academic staff. An online survey was conducted by sigma, Coventry University’s Mathematics and Statistics Support Service, to capture the perceptions and preferences of Coventry University PGRs and research staff around the quantitative training needed to support their research. Key topics of interest include the perceived need for training in specific statistical techniques, understanding statistical outputs and statistical software. The review suggests differences in the needs of PGRs and staff, with PGRs seeking foundational skills and staff requesting more advanced training. Additionally, staff with supervisory responsibilities emphasised the importance of PGRs developing skills in experimental design, data organisation, coding, analysis interpretation and presentation of findings - areas not mentioned by the PGRs. The findings also indicate that January and February are the most favoured months for training, with a significant preference for online delivery across participants. Furthermore, the review highlights the need for tailored workshops to address the diverse requirements of early stage researchers and experienced staff. Recommendations are provided, along with a description of changes implemented at Coventry University to better equip PGRs and staff with essential quantitative skills for their academic and professional careers.
Many universities operate mathematics support; recent debate has included e.g. whether support should be face-to-face or online. However, another relevant question is how many students should be involved in a session. Students have mentioned that it would be good to have many students together so that they can see the answers to questions that others have. However, academics may argue that it is necessary to quiz students in order to specify the problem and this may not be appropriate in front of other students and these students may not benefit. This study will look at circumstances where maths support should be carried out on a one-to-one basis and occasions where it is beneficial for further students to be present.
The mathematical sciences and operational research (MSOR) community in higher education is still largely unprepared to adapt to the rapid rise of generative artificial intelligence (genAI) and its impact on assessment strategies. Whilst in-person exams remain an essential assessment mode for MSOR, take-home assignments are also an integral assessment tool. This work investigates concerns that current assignments are not robust against genAI and the way students use genAI. In this work, we address the following questions: 1) How well can genAI perform in current assignments? 2) To what extent do students currently use AI in take-home assignments? 3) How should assessment strategies evolve given the rapid improvement of genAI? Our research involves an investigation of genAI’s performance in a range of MSOR assignments. We also conducted surveys and discussions with mathematics and statistics students and staff at the University of Warwick. We make recommendation and conclude that genAI represents a catalyst for innovation and assignments, perhaps adapted, should remain a core assessment in MSOR.
Student engagement has been shown to be impacted by a student’s sense of belonging. As part of a wider initiative to enhance belonging amongst students on Mathematics, Physics and Engineering Foundation Year programmes, the Applied Mathematics team implemented a new assessment strategy using group-work and co-created industry contexts. The team co-created the industrial contexts with the whole student cohort, resulting in five industry themes. These themes were then used to develop five versions of a written test, with each version having questions contextualised to one of the five industry themes, and five versions of a group piece that each tackled a problem from one of these industries. Qualitative feedback from module evaluations suggested a positive impact on students. Additionally, the final exam, which was comparable with the previous year, saw an increase in attendance of 21% and increase in average attainment of 10%, suggesting a positive impact on student engagement within the module. However, this formed part of a wider initiative to promote student engagement through student belonging, and therefore these increases cannot be solely attributed to this assessment strategy.
This case study assesses experience in autumn 2023 of permitting the use of Large Language Model Artificial Intelligence (AI) in preparing essays on a module in the history of mathematics. As a check on usage and to ensure academic standards, students were required to complete two paragraphs to accompany their essays explaining their use of AI. These generated qualitative and quantitative data on student familiarity with AI, and ability to use it in a thoughtful and ethical manner, which is reported here. Findings were that over 50% of students rejected AI use, and only 9% used it extensively. There was a weak negative correlation between AI use and essay grade, for which student confidence may have been a confounding factor. The most frequent reasons for rejecting AI were ethical, personal (satisfaction and confidence), and the time needed to correct it.
The integration of artificial intelligence (AI) technologies is revolutionising traditional methods of teaching and learning. The University of the West of England, Bristol, has developed a generative AI policy that encourages AI literacy, personal learning and creativity. In accordance with this policy, we demonstrate use of AI within an established help drop-in service at the university. Data analysis advice from statisticians is provided to students via a newly formed ‘Stats Clinic’ which aims to act as a triage service within the institution’s existing ‘espressoMaths’ service, open to all. With appropriate student preliminary engagement, including the use of AI, the productivity and value of student-academic discussions can be greatly increased. Detail is given of how students can use artificial intelligence to get the most out of pre-visit engagement and therefore ultimately their visit with a statistics professional. Examples where students have applied varying levels of engagement with pre-visit recommended actions are discussed, with empirical evidence from the sessions indicating that those embracing AI are more aware of their data analysis and can comprehend advice more readily.
In 2009 a national study by Ní Ríordáin and Hannigan revealed that 48% of secondary school mathematics teachers in Ireland were classified as ‘out-of-field’ meaning they were certified in subjects other than mathematics. This alarming statistic led to the creation of a national two-year programme to upskill these teachers to qualified status. Based at the University of Limerick, the Professional Diploma in Mathematics for Teaching (PDMT) is facilitated through centres nationwide. Since its inception in 2012, the PDMT has successfully reduced the percentage of out-of-field mathematics teachers to 25%. Initially the program required teachers to have timetabled hours in mathematics ensuring they had some teaching experience in the subject. Many had studied mathematics in their primary degrees; now the PDMT is available to any secondary school teacher seeking qualification in mathematics resulting in a diverse participant profile with many having no prior mathematics teaching. However, research shows that many university students, including mature students, have debilitating mathematics anxiety; and mathematics anxious teachers risk passing on MA to their students. This study hypothesises that mathematics anxiety affects out-of-field mathematics teachers and proposes interventions to support future PDMT students in reducing mathematics anxiety.
This is a report on a workshop held in July 2024 at the University of Greenwich as part of the Higher Education Teaching and Learning Workshop Series supported by three mathematics and statistics professional bodies and learned societies. The report includes the abstracts of the presentations delivered at the workshop.
Digital accessibility, inclusion and diversity are increasingly becoming a priority in Higher Education (HE), however mathematical accessibility for visually impaired people remains an area in need of improvement. Gaps in accessibility for visually impaired students can deter them from pursuing Mathematical Sciences at HE level, put them at a disadvantage in traditional assessments and mask a student’s mathematical ability. Administration, culture and curricula are among the highest-rated obstacles for visually impaired students studying maths implying that alternative pedagogical approaches and technology are needed to address barriers and educators need to understand the challenges faced by visually impaired students to provide appropriate support. The project undertaken at the University of Glasgow started with a consultation with a variety of institutions, professionals, academics and students. This was followed up with a series of discussion groups and culminated with a hybrid workshop. In this paper we will give an overview of the workshop, our findings and discuss the provision of a consistent support system across programmes which can be adapted around individual needs.
