To the casual observer, accounting might not be perceived as the most thrilling side of the finance sector in which to build a career, especially in recent years as a deluge of technology has transformed the industry.

Graduates have flocked to fintech roles, enrolled on training courses to master the world of crypto and digital currencies, or to navigate blockchain. However, as the hysteria subsides, the focus for young professionals may be returning to the status quo.

In fact, as desire for other types of finance-related masters programmes cools, recent data from the Graduate Management Admission Council (GMAC) showed that 72% of masters in accounting programmes reported a growth in applications last year, up from just 43% the year before.

The demand is likely due to accounting being perceived as a ‘safer’ sector to go into. A volatile graduate job market and fluctuations in the demand for other finance roles means that accounting is seen as a stable and secure sector to move into post-graduation, and that career reputation is proving increasingly attractive to students weighing up their options.

However, while accounting will remain a steadfast and essential element of financial services, technology is having a growing influence here too, shaking that safety for many graduates.

The growing impact of AI on the sector

The leading factor driving that unstable job markets is certainly AI. With the growing capability of the technology and its relatively cheap and easy application, many companies are increasingly seeing less need to hire entry level, junior financial roles, as much of the workload can be taken on by AI.

Whether it is data entry, bookkeeping or tax returns, the sector is certainly at risk of being overrun by AI, reducing entry-level opportunities even further. That is why it is so important to adapt when it comes to the skillsets that are being taught in accounting education - and to adapt quickly.

The issue is not just that AI is capable of doing what junior accountants do. Anecdotal evidence from employers suggests that graduates are also leaving university and business schools without the skills that employers are truly looking for.

They may leave their taught programme with the ability to bookkeep, for instance, but lack the soft skills needed to manage a team, communicate complex financial information to non-financial stakeholders, or make strategic decisions under pressure. Closing that gap has to be a priority for programmes.

Rethinking the curriculum with AI in mind

Against this backdrop, accounting education needs to reconsider both what it teaches and how it teaches it. Technical competence will remain essential, but the profession increasingly demands broader capabilities: analytical reasoning, ethical judgement, strategic thinking, and the ability to interpret financial information in complex organisational contexts.

One useful way to think about accounting education is to distinguish between two types of modules (He and Li, 2026).

Type 1 modules focus on core technical knowledge. These typically include financial reporting, auditing, accounting standards, and regulatory frameworks. They teach students the foundational techniques required to understand and prepare financial information.

Type 2 modules, by contrast, emphasise interpretation, analysis, and decision-making. They explore how accounting information informs strategic choices within organisations and how financial data should be interpreted in uncertain or complex business environments. Recognising this distinction is particularly helpful when considering how AI should be incorporated into accounting education.

Which modules should focus on integrating AI?

When it comes to Type 1 modules, AI tools have a clear and practical role to play. These modules cover the routine, process-driven tasks that AI is already handling in the profession.

Rather than teaching students to complete these tasks manually from start to finish, programmes should be introducing practical tutorials that show students how to use AI to perform them more efficiently.

Crucially though, this is not about outsourcing the thinking. Students still need to understand the underlying concepts and principles well enough to oversee AI outputs, spot errors, and apply their own judgement where needed.

AI systems can produce incomplete, biased, or inaccurate results. Accountants must therefore possess the technical knowledge necessary to recognise errors and exercise professional judgement.

For this reason, assessed exams in Type 1 modules should remain closed-book, ensuring students have genuinely mastered the core technical skills rather than learned to rely on AI as a crutch.

Where AI integration is less appropriate

The role of AI is more nuanced in Type 2 modules. These modules deal with complex business analysis, strategic decision-making and financial interpretation – areas where AI can assist, but cannot lead.

ChatGPT and similar basic AI tools can help students gather and process large volumes of financial and non-financial data more efficiently, and can offer perspectives on complex problems. But the judgement, critical reasoning and contextual understanding required to actually solve those problems must remain the student’s own.

Take financial statement analysis as an example. A student might use AI to support the forecasting element of their work, where uncertainty is high and multiple scenarios need to be modelled.

But the strategic analysis, the accounting judgement and the final investment recommendation require human reasoning that AI cannot reliably replicate. Students should be taught to consult AI as one input among many, not to simply follow where it leads.

This has implications for assessment too. Take-home assignments with open-ended, sophisticated questions are well suited to Type 2 modules, and AI use can reasonably be permitted in order to gather materials and evidence, but since the quality of thinking and analysis is what is being tested, work should be completed by human intelligence.

Where questions are more straightforward and AI could largely complete the work itself, the AI approach is far more appropriate but, again, final conclusions should be made with human analysis and rationale.

How can academia make this shift?

Embedding AI meaningfully into accounting programmes requires more than bolting on a module about technology. It calls for a genuine rethinking of how existing modules are taught and assessed.

Faculty need to be equipped with a working understanding of AI tools themselves, not to become technologists, but to integrate these tools into their teaching in a way that is purposeful rather than superficial.

Bringing in practitioners who work at the intersection of AI and accounting can help bridge the gap between the classroom and the profession, and more schools should be doing this.

Dissertation modules also deserve attention. Like Type 2 modules, dissertations involve complex data analysis and original thinking – areas where AI can genuinely support the research process, from summarising literature to helping structure arguments.

But with that comes responsibility. Students using AI in their research must take full ownership of accuracy and transparency, and taught programmes should have clear, consistent policies in place to ensure that is the case.

Accounting education is at an inflection point. Applications are rising, the profession is evolving and AI is reshaping what it means to work in finance. The programmes that will serve students best are not those that ignore these changes, nor those that uncritically embrace every new tool, but those that are thoughtful about where AI adds value and where human judgement remains irreplaceable.

About the author

Dr Guanming He is a professor at Durham University Business School

Reference: He, G., & Li, A. Z. (2026). Advancing Accounting Education: The Role of AI Technologies. In V. Q. Trinh & T. N. Pham (Eds.), The Future of Accounting and Finance: Embracing Technology, Digitalisation, Sustainability, Education, and Employability (pp. 381-398), Springer Nature.