The statistics are stark. As of March 2024, 28.6 percent of NHS staff came from BAME backgrounds, but only 12.7 percent held very senior management positions. In clinical roles, BAME staff are concentrated in junior bands, making up 42.3 percent of Band 5 posts but just 12.0 percent of Band 9. Academia shows an even sharper gradient: in the biosciences, only five of 1,345 professors were Black as of 2023, according to Higher Education Statistics Agency data. Of nearly 23,000 UK professors, fewer than 45 are Black women. The pattern persists at the point of entry, too: between 2010 and 2016, only 36 percent of BAME applicants to Russell Group universities were offered places, compared with 55 percent of White applicants, and in 2024 the University of Oxford’s biomedical sciences course made offers to just 6.7 percent of BAME applicants against 13.9 percent of White applicants.

Researchers Ruth Gyereh Dogwul and Eiman Aleem of London South Bank University, together with Bernine Idowu of the University of West London, published the case study in Frontiers in Education on 16 October 2026. It documents a series of targeted interventions embedded within the university’s Institute of Biomedical Science-accredited BSc (Hons) Biomedical Science programme, launched in 2020, and pairs them with an anonymous survey of alumni and current students. The study is framed around three recurring barriers: limited access to the discipline, a weak sense of belonging, and a scarcity of visible role models. The university’s setting makes it a natural laboratory for the work. In 2025, 66 percent of LSBU students were from BAME backgrounds, in a region of high ethnic diversity and historically low participation in higher education, and roughly 60 percent of the biomedical science academic staff themselves come from BAME backgrounds, a notable exception to the 37 percent BAME share across the university’s overall staff.

The flagship initiative is the Black in Biomedical Science Undergraduate Conference, a collaboration between LSBU and the University of West London. The inaugural event, themed Changing the Story, was held in 2023 at UWL; the second took place at the University of Cambridge in 2024, and the third returned to LSBU in 2025 under the banner Empowering the Black Biomedical Scientist of Tomorrow. Around 95 percent of attendees were Black students, with the remainder mostly from other minority groups. Delegates hear keynote lectures from senior professionals describing how they navigated academia and healthcare, take part in dedicated networking sessions and afternoon workshops on career development and confidence-building, and leave with a programme booklet listing speakers and participants to make post-conference connections easier. Feedback collected from roughly 100 respondents across the three conferences was consistently positive, with participants singling out networking, exposure to diverse career pathways and access to role models as the most valuable elements.

Attendees’ own words capture why representation matters so much. One wrote that seeing young Black men and women in spaces of authority fills you with confidence and hope, and provides assurance that there are people looking out for you who want you to succeed. Another described being surrounded by smart Black individuals as transformative, while a panellist’s story left a participant with the conviction that you can come from any background and still go far in life, and that even failure is not the end because the future still awaits. These responses align with prior qualitative research showing that social isolation, stereotype threat and unequal access to informal academic networks significantly hamper Black students in medical and biomedical programmes in London. The conference team says future iterations will focus on structured mentoring schemes and longitudinal evaluation of participant outcomes.

The second strand of the case study targets the pipeline itself, reaching students years before they apply to university. One workshop brought approximately 15 female pupils aged 7 to 13 from BAME backgrounds from a local primary school onto campus for a DNA extraction practical using fruit samples, DNA double-helix modelling with confectionery materials, a careers session, and a laboratory tour, all supported by student ambassadors and Level 6 biomedical science students who shared their own experiences. A second event, staged for Biomedical Science Day with support from the Institute of Biomedical Science, brought 27 students aged 14 to 16, mostly from BAME backgrounds, from Lambeth College and South Bank College into LSBU’s laboratories. They examined blood films, malaria-infected slides and sickle cell morphology under the microscope, performed simulated ABO blood typing, watched a full blood count demonstration on an automated Sysmex haematology analyser, and worked through a forensic science workshop using fingerprint analysis, fibre comparison and basic DNA profiling to identify a mock suspect.

Written feedback collected on paper at the end of each outreach event showed that students valued the hands-on laboratory experiences above all, with microscopy, blood grouping and the full blood count consistently identified as the most engaging components. Participants also highlighted the benefit of interacting with university staff and students, which helped demystify higher education and laboratory-based careers. The authors note that the combination of scientific content with informal mentoring supports both cognitive and affective dimensions of learning, including confidence and belonging. They are candid about the limits of the evaluation, however: feedback was limited to immediate post-event responses, and future iterations would benefit from structured pre- and post-event questionnaires and longitudinal tracking to assess changes in knowledge, attitudes and career aspirations over time.

The survey of alumni and Level 5 and 6 students, prepared using Qualtrics and approved by the LSBU Ethics Committee under registration ETH2425-0200, comprised 24 questions across four sections covering demographics, course experience, career progression and suggestions. Approximately 48 students, around 40 percent of the cohort, responded. Among them, 76 percent were from BAME backgrounds, including 23 percent Black or African-Caribbean, 35 percent Asian, 8 percent Arab and 10 percent mixed ethnicity; 81 percent identified as female and 83 percent were aged 18 to 25. Forty percent were the first in their family to attend university. The results were largely encouraging: 94 percent rated the course positively, 94 percent felt always or often supported by academic staff, 67 percent found the learning environment very inclusive, and 78 percent reported access to BAME role models or mentors. Forty percent had attended a widening participation event and said it positively influenced them, while a further 31 percent had not attended but would be interested.

The survey also exposed where barriers persist. Sixty-three percent of respondents first learned about biomedical science as a career option only at sixth form or college, with just 25 percent aware of it during secondary school, suggesting that outreach needs to reach students even earlier. Financial constraints were the most frequently cited personal barrier, named by 33 percent of respondents, ahead of lack of information about the profession at 13 percent and limited role models or mentors at 10 percent. Career aspirations were diverse, with 48 percent heading into healthcare professions, 29 percent into research and 13 percent into pharmaceutical or biotech industry roles. Seventy-one percent found the employability skills embedded in the curriculum helpful for career progression, and 81 percent felt the course had prepared them for their intended careers. Placement feedback was mixed, with international students in particular reporting barriers linked to immigration status, and only 15 percent of respondents had actually completed a placement year, all but one in a hospital or clinical setting.

Free-text suggestions for improvement clustered into six themes: broader career guidance beyond biomedical science, more placement and work experience opportunities, requests for more practical and less theoretical content, stronger academic and pastoral support with early identification of struggling students, greater representation in career talks and mentoring particularly for BAME and international students, and positive reflections praising staff dedication and events such as the Black in Biomed conference. The authors conclude that inclusive, career-focused initiatives embedded within the curriculum can support equity and representation, and argue that their model is easily transferable to other programmes. Their stated priorities are earlier career guidance, relatable role models, hands-on experience and targeted financial support through scholarships and bursaries. As they put it, to truly widen participation, institutions must embed equity into recruitment, mentorship, career and leadership development, ensuring that BAME professionals are not only welcomed into the system but empowered to lead within it.

Subject of Research: Strategies for widening participation in the biomedical science at London South Bank University—a case study

Article Title: Strategies for widening participation in the biomedical science at London South Bank University—a case study

Article References: Dogwul, R. G., Idowu, B., & Aleem, E. (2026). Strategies for widening participation in the biomedical science at London South Bank University—a case study. Frontiers in Education, 11, Article 1748405. https://doi.org/10.3389/feduc.2026.1748405

Image Credits: AI Generated

DOI: 10.3389/feduc.2026.1748405

Keywords: biomedical science, widening participation, higher education, BAME representation, mentorship, STEM outreach, London South Bank University, equity in science, Strategies, widening, participation, biomedical

The timing of the proposal is significant. Higher education systems worldwide are straining under rising application volumes, constrained resources, and intensifying expectations around widening participation. In the United Kingdom alone, the 2023 UCAS cycle involved more than 750,000 applicants and nearly three million applications. Admissions systems designed for smaller applicant pools and standardised qualifications are buckling under the load, and AI promises scalability. Yet Alenezi’s review of the literature reveals a troubling pattern: most AI-enabled admissions scholarship evaluates systems purely on prediction accuracy, enrolment optimisation, or processing efficiency, while paying scant attention to learner agency, transparency, or educational equity.

The risks are not hypothetical. The paper documents how algorithmic systems trained on historical data can encode and perpetuate existing inequalities. Amazon’s experimental CV-screening tool systematically downgraded applications from women because it had learned from historically male-dominated hiring patterns. The COMPAS recidivism tool demonstrated racially disparate predictions. And the UK’s 2020 A-level algorithm depressed grades for students from lower-performing schools, sparking a national controversy. In admissions, the danger takes specific technical forms: representation bias when training datasets under-represent particular groups, measurement bias when proxy variables like postcode or school type capture group membership rather than merit, and feedback bias when predictions shape institutional actions that then create self-fulfilling cycles.

HCPAF responds with an architecture of five interconnected dimensions: learner profiling and contextual understanding; explainable AI decision support; equity and inclusion auditing; human oversight and shared decision-making; and personalised transition and success support. Crucially, the framework treats these dimensions as recursive rather than sequential. Profiling choices shape what can be explained and audited; fairness findings may force revisions to model features; human review can trigger model correction; and transition outcomes feed back into assumptions made at the point of admission. The result is a system of mutual constraints rather than a frictionless pipeline.

Technically, the framework leans on established explainable AI techniques such as LIME and SHAP, which generate interpretable accounts of how input variables contribute to model outputs. But Alenezi, drawing on the work of Khosravi and colleagues, insists that explainability must be calibrated to its audience. An explanation intelligible to a data scientist may be opaque to an admissions officer or an applicant, so the framework calls for layered explanation architectures serving multiple stakeholder groups simultaneously. All AI-generated recommendations must be accompanied by plain-language explanations, consistent with GDPR Article 22 rights and the EU AI Act’s requirements for high-risk systems in education.

The framework is equally candid about trade-offs that much of the AI-in-education literature glosses over. Richer contextual data can support fairer decisions, but collecting more socioeconomic, demographic, and behavioural information increases privacy risk. Postcode-level measures such as POLAR4 quintiles can identify structural disadvantage, yet the same variables may act as proxies for ethnicity, class, or disability — and removing them entirely can make disadvantage statistically invisible. HCPAF’s answer is neither uncritical inclusion nor blanket exclusion, but counterfactual and subgroup sensitivity testing, with contextual variables used preferentially to expand opportunity rather than deny access. Similarly, the framework acknowledges that demographic parity, equalised odds, and individual fairness cannot always be satisfied simultaneously, and that carrying admissions data into student-support systems may enable earlier help while creating risks of labelling and surveillance.

One of the study’s most distinctive conceptual contributions is its treatment of trust. Rather than assuming that transparency automatically builds confidence in AI systems, Alenezi frames trust as a relational, calibrated, and empirically contingent construct. More information does not necessarily generate more trust: disclosure of uncertainty, data limitations, or unequal error rates may appropriately reduce confidence. The relevant objective is calibrated trust, in which applicants and staff understand what the AI can and cannot do, can challenge its outputs, and have credible evidence that errors and inequities will be detected and remedied. Explainability, fairness auditing, and human oversight may reinforce one another — or they may reveal justified grounds for distrust.

Generative AI occupies a carefully bounded role within the framework. While predictive machine learning supports profiling and fairness monitoring, large language models are positioned mainly as an interaction and explanation layer: translating model outputs into applicant-accessible language, providing source-grounded conversational guidance about programmes and procedures, and personalising pre-arrival information. Recognising that LLMs can hallucinate, reproduce bias, and generate confident but incorrect advice, HCPAF requires retrieval from authoritative institutional sources, logging and audit of outputs, clear disclosure that applicants are interacting with AI, and escalation to human advisers wherever financial, disability, visa, safeguarding, or admissions-condition issues arise. Notably, the framework treats AI-assisted personal statements as a governance problem rather than an automatic integrity violation, urging institutions to avoid unreliable AI-detection tools and to ensure applicants are not disadvantaged by unequal access to language models.

The theoretical scaffolding is deliberately broad, integrating six traditions: Shneiderman’s human-centred AI, responsible AI and algorithmic governance, Sen’s Capability Approach, educational equity theory, Value-Sensitive Design, and human–AI collaboration theory. The Capability Approach in particular reframes the evaluation question: admissions systems should be judged not only by whether they predict academic success accurately, but by whether they expand or constrain the substantive freedoms of diverse learner populations to access and flourish within higher education. The framework also identifies normative alignments with Sustainable Development Goals 4, 10, and 16, though Alenezi is careful to stress that these are alignments of principle, not evidence of impact.

The author is refreshingly explicit about the framework’s limits. HCPAF is a conceptual synthesis, not an empirically validated admissions model, and prospective evidence on live AI-assisted admissions remains thin. The paper’s illustrative scenarios — a post-92 university using contextualised offer-making with monthly equity audits, a research-intensive university deploying a generative AI advising portal with human review of consequential guidance, and a specialist provider connecting admissions profiles to early-warning retention systems — are explicitly hypothetical design propositions rather than documented deployments. Until validation evidence accumulates through case studies, participatory co-design, and longitudinal evaluation, HCPAF should be used as a governance heuristic for interrogating AI-enabled admissions, not as proof that such systems will improve equity or trust. That honesty may prove to be the framework’s most important feature: at a moment when institutions face intense pressure to adopt AI, it offers a roadmap for doing so on educational rather than purely institutional terms.

Subject of Research: A human-centred AI and learning analytics governance framework for equitable personalised higher education admissions

Article Title: A human-centred artificial intelligence and learning analytics framework for equitable personalised higher education enrolment

Article References: Alenezi, A. F. (2026). A human-centred artificial intelligence and learning analytics framework for equitable personalised higher education enrolment. Discover Education, 5(1), Article 1084. https://doi.org/10.1007/s44217-026-02221-2

Image Credits: AI Generated

DOI: 10.1007/s44217-026-02221-2

Keywords: human-centred AI, learning analytics, higher education admissions, educational equity, explainable AI, generative AI, learner agency, algorithmic bias, responsible AI governance, personalised learning, widening participation, student success