Generative AI in Malaysian Pharmacy Education: A Call for Responsible Integration

Generative artificial intelligence (GenAI) has rapidly transformed higher education worldwide. Since the public release of large-language-model chatbots in 2022, an increasing number of students have integrated these tools into their daily academic practice [1]. In pharmacy education, this shift presents both opportunities and risks. The challenge is no longer whether GenAI can be used, but how it should be used responsibly and ethically to support learning without compromising critical thinking skills as well as the professional values of integrity and accountability in patient-centred care. This Letter is directed primarily at policymakers, pharmacy schools, and professional bodies in Malaysia, as their coordinated leadership will be critical in shaping responsible integration.

Recent surveys across higher education show widespread GenAI use for drafting, proofreading, summarising, and problem-solving, a trend that is similarly evident among pharmacy students [2]. In a multi-country survey involving 2009 pharmacy students, including Malaysian respondents, Elnaem et al. (2025) reported that ChatGPT and QuillBot were the most frequently used tools [3]. Adoption was driven by perceived usefulness and ease of use, while price value and habitual use had minimal influence. The findings indicated that Malaysian students demonstrated relatively higher acceptance, with male students reporting greater usage than female counterparts. Alarmingly, fewer than 10% prioritised legal and ethical training, and more than one-fifth admitted over-reliance on GenAI for assignments. International guidance, such as from UNESCO (2023), emphasises that GenAI can enhance learning if its use is transparent and critically evaluated, but unacknowledged reliance as a substitute for original work risks undermining academic integrity [4].

This gap between enthusiasm and responsible integration is concerning. While students recognise GenAI’s utility, they may underestimate issues such as bias, privacy breaches, or overdependence on machine outputs. Without structured guidance, pharmacy graduates risk becoming adept at prompting tools but unskilled in independent reasoning, potentially compromising future patient safety. AI literacy in pharmacy must extend beyond technical familiarity. It should encompass three key elements: autonomy, or the ability to make defensible decisions with or without AI support; competence, or the methodological awareness to use AI responsibly; and human-centredness, or the integration of empathy, communication, and professional judgement into practice. Without these, GenAI risks producing graduates who are skilled prompt engineers but less capable clinicians [5].

Considering the need to cultivate higher-order thinking and professional judgement, constructivist teaching strategies provide a useful pathway. For example, GenAI can scaffold learning in complex areas such as pharmacokinetics, therapeutics, or patient counselling by generating simulations, examples, or practice dialogues. However, outputs should never be accepted at face value. Students should be trained to critique, verify, and refine AI-generated content, while teachers emphasise documentation and reasoning. This approach turns GenAI into a catalyst for deeper learning rather than a shortcut to superficial answers [6].

Nevertheless, faculty readiness is a critical issue. While students often adopt GenAI quickly, lecturers remain uncertain about its reliability, data privacy, or teaching value [6]. Without targeted training, educators risk losing credibility as students bypass them in favour of AI. Beyond readiness, uncritical reliance on GenAI may erode essential professional skills such as critical thinking, calculation accuracy, evidence appraisal, and patient communication [7]. These risks are compounded by AI hallucinations which can mislead students if the outputs are accepted without scrutiny [8]. Moreover, educational inequities may also widen, as premium AI services remain unaffordable to some learners, giving wealthier peers an advantage in assessments or research tasks. Ethical training must therefore address not only plagiarism and citation but also equity, accountability, and patient confidentiality.

In preparation for these challenges, Malaysia has already taken proactive steps in ensuring responsible governance of AI. In 2023, the National Artificial Intelligence Office (NAIO) released the National Guidelines on AI Governance and Ethics, outlining principles of fairness, safety, privacy, inclusiveness, transparency, accountability, and human benefit in the development and deployment of AI [9]. While voluntary, these guidelines provide universities with a framework to craft their own policies and classroom practices.

For instance, a local university has begun to integrate AI, data analytics, and health informatics into both digital health and pharmacy curricula, paired with training in communication, ethical reasoning, and critical thinking to preserve humanistic aspects of care [10]. Meanwhile, Monash University Malaysia offers a Master of Artificial Intelligence, a postgraduate coursework program designed to cultivate advanced competencies in technical, ethical, and applied dimensions of artificial intelligence [11].  These examples demonstrate how Malaysian universities can act as early adopters while preserving professional standards. Beyond governance, Malaysia’s 13th Malaysia Plan (RMK-13) has also prioritised digitalisation and expanded internet access as national development goals [12]. These commitments create an enabling environment for higher education institutions to integrate AI literacy and digital health into their curricula.

Yet challenges remain in the Malaysian context. Faculty resistance limited institutional facilities, and uneven readiness across public and private universities could hinder effective integration. Without a coordinated top-down strategy, early adopters may progress while others lag, thus widening the gap. Hence, a centralised policy framework, aligned with RMK-13 and the NAIO guidelines, is needed to ensure equitable and systematic implementation nationwide.

Looking forward, we highlighted several priorities which deserve urgent attention to strengthen the integration of AI into pharmacy education (Figure 1). First, AI and data literacy should be embedded across the pharmacy curriculum rather than confined to elective modules [13]. Concepts such as algorithm bias, limitations of large-language models, and confidentiality must be woven into core subjects including pharmacotherapy, pharmaceutics, and clinical pharmacy.

Secondly, faculty assessments must be reoriented to prioritise reasoning and accountability. Potential strategies include viva voce defences of AI-assisted work, oral structured clinical examinations that incorporate AI-modified cases, and iterative assignments requiring version control. These approaches discourage uncritical outsourcing while fostering professional judgement.

Thirdly, faculty capacity must also be strengthened. Universities should organise regular workshops on AI pedagogy, ethics case discussions, and interdisciplinary teaching to ensure staff remain ahead of students and maintain credibility in guiding responsible use. Faculty development initiatives co-designed with computer scientists and clinicians can enhance educators’ confidence while ensuring that AI integration is aligned with the realities of pharmacy practice.

Finally, professional bodies including the Pharmacy Board Malaysia and Malaysian Pharmacists Society should collaborate with universities to issue sector-specific guidelines. Aligning these with the NAIO’s national framework would create consistency, reassure educators, and set clear expectations for students.

GenAI has already become part of how Malaysian pharmacy students engage in their learning. Evidence has pinpointed both enthusiasm and risks, including strong perceived usefulness, but weak ethics awareness and instances of over-reliance. Malaysian pharmacy education can nurture professionals who are proficient with AI, ethically grounded and steadfastly patient-centred by acting now. We urge educators, policymakers, and professional bodies to treat GenAI as a structural shift in pharmacy education and act decisively to integrate it responsibly.

CONFLICT OF INTEREST

The authors declare no conflict of interests.

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