AI for Biotech Futures: Connecting Biotech Companies and Educators to Shape an AI-Ready Workforce

Ashley, J ORCID: 0000-0002-7062-1019, Mosses, D, Olorunniji, FJ ORCID: 0000-0001-9389-2981, Sexton, DW ORCID: 0000-0003-3344-3150, McStay, GP ORCID: 0000-0003-1363-8719, Qi, B ORCID: 0000-0002-9425-935X, Sarker, MH ORCID: 0000-0003-4698-2161, Nakouti, I ORCID: 0000-0001-9438-6300, Hobbs, G ORCID: 0000-0002-1284-5903, Ross, K ORCID: 0000-0003-0252-1152 and Rahman, PKSM ORCID: 0000-0001-7416-4372 (2026) AI for Biotech Futures: Connecting Biotech Companies and Educators to Shape an AI-Ready Workforce. Technical Report. Liverpool John Moores University.

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Abstract

Artificial intelligence is rapidly transforming biotechnology, with applications now spanning drug discovery, protein and molecule design, bioprocess optimisation, quality management, regulatory affairs, scientific writing and diagnostics. As biotechnology becomes increasingly data-driven and computational, graduates entering the sector will need to demonstrate not only subject knowledge, but also the ability to use AI tools effectively, ethically and critically.

This white paper examines the emerging AI skills gap between biotechnology education and industry expectations. It identifies three key stakeholder groups: employers, universities and students. Each group has different expectations of AI use, but greater collaboration between them is needed to ensure that biotechnology graduates are prepared for an AI-enabled workforce.

The paper highlights several challenges facing higher education. First, unequal access to AI tools risks creating an uneven playing field for students, particularly where subscription-based tools offer better performance than free versions. Second, AI has disrupted traditional assessment methods such as essays, lab reports, literature reviews and online exams, raising concerns about whether current assessments still measure genuine student learning. Third, the ethical use of AI remains unclear for many students and staff, especially in relation to academic integrity, AI declarations, hallucinated references and the limitations of AI detection tools. Finally, there is a subject-specific AI training gap, as many universities provide only general AI guidance rather than biotechnology-focused training.

To address these challenges, the paper recommends that universities provide equitable access to appropriate AI tools for both students and staff. It also argues that assessments should be redesigned either to prevent inappropriate AI use or to encourage transparent and productive AI collaboration. Alternative assessment formats, such as bioprocess design dossiers, industrial case-study investigations, quality-by-design briefs, public-facing explainers and AI-supported data analysis tasks, may better reflect the skills required in biotechnology workplaces.

The paper further recommends that AI ethics and academic integrity should be embedded into student training from the start of degree programmes. Students should be taught how to evaluate AI outputs, verify sources, recognise hallucinated references, declare AI use appropriately and understand the risks of over-reliance on AI-generated content. Staff should also receive structured training so that they can use AI confidently, redesign assessments and support students effectively.

A two-level approach to AI skills development is proposed. All biotechnology students should receive basic AI literacy training, including prompt engineering, literature searching, writing support, revision, scientific image generation, data handling and critical evaluation of AI outputs. More advanced AI skills, such as coding, machine learning, AI model development, large dataset analysis and biotechnology-specific AI applications, should be embedded into specialist modules, practical classes and final-year projects.

Overall, this white paper argues that universities must act quickly to embed AI skills into biotechnology education. By improving access to AI tools, redesigning assessments, strengthening ethical guidance and working more closely with industry, higher education can help close the AI skills gap and prepare graduates for responsible, effective and productive careers in the biotechnology sector.

Item Type:	Monograph (Technical Report)
Uncontrolled Keywords:	academic integrity; AI ethics; AI skills gap; artificial intelligence; assessment design; biotechnology education; biotechnology workforce; higher education
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine > RS Pharmacy and materia medica T Technology > T Technology (General)
Divisions:	Pharmacy and Biomolecular Sciences
Publisher:	Liverpool John Moores University
Date of acceptance:	13 July 2026
Date of first compliant Open Access:	14 July 2026
Date Deposited:	14 Jul 2026 09:19
Last Modified:	14 Jul 2026 09:19
URI:	https://researchonline.ljmu.ac.uk/id/eprint/28996

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