AI and future graduates: rethinking study skills and employability

Zheng Feei Ma is Senior Lecturer in Public Health in the School of Health and Social Wellbeing, College of Health, Science and Society, at the University of the West of England, Bristol. He provides strategic leadership in learning and teaching through the application of evidence-based and inclusive pedagogical approaches to curriculum enhancement, assessment design, and student support.

Introduction

Artificial intelligence (AI) has emerged as a transformative technology in higher education and labour market. The incorporation of AI into these areas has redefined the required skills by students and the employability conditions. Students have not only used AI tools such as ChatGPT and Copilot to support their learning processes, but also to structure their CVs and job application cover letters. To date, the conversation around the use of AI in higher education is increasingly polarised. Some academic staff members see the use as of AI as a revolutionary education tool, while others see it as a threat to academic integrity. As AI is becoming increasingly embedded within higher education and professional practice, the critical question is no longer whether students should use AI, but whether they can use it critically, ethically and effectively.

AI as a study partner, not a shortcut

When AI tools are used critically and thoughtfully, they have the potential to enhance the study practices and learning experiences of students. Although these AI tools are useful to support the generation of ideas, clarification of complex concepts and organisation of academic writing for students, they should not be seen as a substitute for the learning process itself. To make AI as a true study partner, here are some practice ways:

Ask ‘why’ and ‘how’, rather than accepting AI-generated responses at face value.
Engage in active learning by completing the assessment tasks independently before using AI to review them.
Critically evaluate AI-generated feedback and reflect on the rationale behind these suggestions.

Rethinking study skills in the age of AI

Although traditional study practices such as note-taking and memorisation are still important to provide a foundational baseline, there is an increasing emphasis on the development of higher-order cognitive and epistemic skills in the AI-enhanced educational environment. Two main competencies are:

Critical thinking: AI-generated outputs can be highly convincing, even when they are biased and inaccurate. Students may feel that they understand a topic because AI tools have simplified the topic for them, without engaging with the complexity of the topic. Cognitive struggle is a fundamental aspect of intellectual development. Processes such as questioning, drafting, revising and grappling with difficult ideas are essential for developing disciplinary expertise.
Epistemic literacy: Al tools do not ‘understand’ knowledge, but it predicts statistically probable language patterns based on large datasets. AI tools do not possess understanding, judgement, or epistemic awareness in the same way humans do. Therefore, students need to develop epistemic judgement skills for navigating the AI-enhanced learning environment effectively, especially in public health and healthcare disciplines. For example, students must be able to understand how knowledge is produced and critically distinguish evidence-based claims from plausible information.

AI and employability: beyond ‘digital skills’

Students may assume that simply knowing how to use AI tools will improve their employability. While AI literacy and digital competence are important, employers are also looking for graduate who can think critically and exercise sound judgement in complex situations. As AI tools increasingly automate routine administration tasks, some entry-level graduate roles may be redefined and displaced. Consequently, graduates may face greater pressure to demonstrate their unique human capabilities that AI tools cannot easily replicate. In professions such as public health and healthcare, employability may depend not only on whether graduates can use AI tools, but whether they can apply their professional practice to critically interpret and challenge AI-generated output when making decision in complex real-world situations.

Thoughts for the future

To maximise both academic success and employability, students need to use AI tools with more thoughtful and strategic approaches. This may include:

Using AI tools to cross-examine complex and contested issues, for example comparing how AI frames public health inequalities, or ethical healthcare decisions across different social, political or cultural contexts.
Testing AI-generated recommendations against real-world professional constraints, such as limited resources, ethical tensions, conflicting evidence, or population-specific considerations that AI tools may overlook and oversimplify.
Reflecting on how AI may reproduce dominant narratives or structural biases, particularly in areas such as public health, global development, race, gender, ageing or health inequalities, where knowledge is often shaped by historical, social and cultural contexts.

Ultimately, the future educational challenge may not simply be whether students can use AI tools effectively, but whether they can continue to think critically and independently in a world increasingly influenced by AI-generated information and automated decision-making.