This semester, I opened an AI tool before I opened a textbook. I used it to untangle difficult concepts, structure my thinking, and navigate research I did not yet understand. So did most of my peers. We did not think twice about it.

But here is what we did not think about: what happens when we graduate into a workforce where employers are doing the same thing - faster, cheaper, and without needing a salary?

The data tells a story we are not ready for.


Chart 1 of 5
Source: QILT Graduate Outcomes Survey 2024. Hover over data points for values. Dashed lines mark COVID-19 (2020) and ChatGPT launch (November 2022).

After years of steady recovery, graduate full-time employment has fallen sharply - from a peak of 79.0% in 2023 to just 74.0% in 2024. The decline began precisely as AI tools became widely available. Correlation is not causation. But the timing is worth understanding.


Chart 2 of 5
Source: Stanford HAI AI Index 2024. Diamond markers = landmark AI milestones. Composite of MMLU, HumanEval, and MATH benchmarks normalised to human = 100. Hover for values.
“In 2012, AI could barely play Pong. By 2024, it had surpassed human performance on multiple graduate-level benchmarks - legal reasoning, mathematics, and code generation included.”

This is not a gradual shift. The capability improvements since 2020 represent a structural change in what AI can do - and the tasks it now performs well are precisely those forming the core of most graduate careers.


Chart 3 of 5 - Multivariate
Sources: QILT Graduate Outcomes Survey 2024; WEF Future of Jobs Report 2025; DESE 2023. AI risk scores map WEF occupational displacement estimates to QILT fields. Bubble size = approximate annual domestic graduates. Hover for values.

Health, Education, and Engineering graduates enter a market that still needs them urgently. But graduates in Management, Commerce, Law, and Creative Arts face a compounding challenge: below-average employment and above-average AI exposure. Around 130,000 Australians graduate annually in these higher-risk fields.


Chart 4 of 5 - Multivariate
Source: WEF Future of Jobs Report 2025. Demand change = net shift in proportion of employers rating each skill as critical by 2030. AI disruption level = degree to which AI can replicate the skill (author categorisation based on WEF report and Oxford Martin Programme research). Hover for values.
“The skills most at risk are not the ones universities teach least - they are the foundational skills we spend years on: writing, numeracy, data entry, and coordination.”

The skills declining most sharply are precisely those forming the core of many degree curricula. Meanwhile, the skills growing most rapidly - AI literacy, creative thinking, empathy - are treated as electives at most universities, not foundations.


Chart 5 of 5 - Multivariate
Sources: QILT GOS 2024; WEF Future of Jobs Report 2025. Skills utilisation = % of graduates who agree degree skills are used in current work (QILT CEQ). AI Exposure Score maps WEF occupational AI exposure to QILT fields (higher = greater exposure). Colour scale inverted for AI Exposure so green = favourable. Hover for values.

The fields carrying the greatest AI exposure are also where graduates feel least equipped and where full-time employment is hardest to find. This is the compounding disadvantage - AI risk stacked upon declining employment and below-average skills utilisation.


What This Means

This is not a story about whether AI is good or bad. It is a story about preparation, timing, and honesty.

Three things need to change.

1. Curricula must shift toward skills AI cannot replicate. Critical thinking, ethical reasoning, interpersonal communication, and AI fluency are not soft skills. In the next decade, they will be the hard ones.

2. Students in high-risk fields deserve honest information. A student choosing a Creative Arts or Commerce degree in 2026 is not choosing a safe path. They have a right to make that choice knowing what the data shows.

3. Australia needs a national conversation about graduate preparation for an AI economy. The data exists. The research is clear. What is missing is the urgency to act on it.


References

Maslej, N., Fattorini, L., Perrault, R., Parli, V., Reuel, A., Bryson, J., Clark, J., Etchemendy, J., Ligett, K., Lyons, T., Manyika, J., Niebles, J. C., Shoham, Y., Wald, R., & Clark, J. (2024). The AI index report 2024. Stanford University Human-Centered Artificial Intelligence. https://aiindex.stanford.edu/report/

Quality Indicators for Learning and Teaching. (2024). 2024 Graduate Outcomes Survey national report. https://www.qilt.edu.au/docs/default-source/default-document-library/2024-gos-national-report.pdf

Quality Indicators for Learning and Teaching. (2024). 2024 GOS national report tables [Data set]. https://www.qilt.edu.au/docs/default-source/default-document-library/gos_2024_national_report_tables.zip

World Economic Forum. (2025). The future of jobs report 2025. https://reports.weforum.org/docs/WEF_Future_of_Jobs_Report_2025.pdf

Acknowledgement of Generative AI Use

Generative AI tools (Claude, Anthropic) were used in the preparation of this assignment to assist with narrative structuring, R code suggestions for plotly interactivity, and initial outline drafting. All data sourcing, analytical interpretations, design decisions, chart encoding choices, narrative content, and final R code were developed and verified independently by the author. AI use is acknowledged in accordance with RMIT Library guidelines (RMIT Library, 2024).

RMIT Library. (2024). Artificial intelligence (AI): Acknowledgement and referencing guidelines. https://rmit.libguides.com/referencing_AI_tools