The Digital Education Council has just released a new (and pleasingly short and succinct) piece of work Student Voices on AI: An Actionable Guide for Institutions and Faculty which draws on global student perspectives on AI and then turns them into practical guidance for tertiary education institutions. A central theme is that students are already using AI extensively, but ambiguity around institutional rules forces them to do so discreetly.
In the absence of clear guidance, students are relying on their own personal ethics, resulting in inconsistent practice. The students DEC spoke with (and they were a small number but globally representative) called for institutions to:
- “establish explicit AI-use guidelines at the outset of each course
- normalise transparent AI use disclosure through clear declaration statements, and
- reduce less reliance on AI detection tools.”
As is so often the case when educational institutions seek student feedback and guidance – they hit the nail on the head.
Students are calling for “discipline-sensitive AI policies that reflect how knowledge and skills are developed differently across fields”. That is:
- in fields like the humanities, psychology and law which require high levels of critical thinking there may be a need for “clearer boundaries” on AI use, while
- in “applied fields” like STEM and business there may be more opportunities for “more experimental and integrated use of AI to reflect its growing role in professional practice.”
Students also argue that AI literacy must move “beyond prompt engineering” and instead help students to learn how to evaluate AI output (ie to detect bias or hallucinations).
Against that backdrop the Australian government’s National AI Plan includes a key role for Future Skills Organisation (the Jobs and Skills Council for the ICT, Business and Finance sectors).
FSO is conducting an economy wide consultation on AI skills development which will deliver “insights and implications” under the National AI Plan. They are aiming to:
- “Build a clearer picture of current AI skills and capability across the economy
- Draw together existing research, initiatives and evidence on AI skills development
- Identify where capability is emerging, where gaps persist, and where further attention may be needed
- Inform future policy and system decisions to build workforce capability.”
