Equipping Educators and Students for an AI-First World

Michael Sankey, Adjunct Professor, Charles Darwin University

Michael Sankey, Adjunct Professor at Charles Darwin University, specializes in e-learning pedagogies, multimodal design and authentic assessment. His 30+ year career in higher education focuses on enhancing student learning opportunities through blended approaches. An active member of ASCILITE, HERDSA and IVLA, his recent research examines artificial intelligence in education, quality systems, multimedia representation and blended learning. Drawing on his background in instructional design and academic development, he leads training in leadership and benchmarking across the higher education sector. His passion is developing leaders leveraging technology to transform teaching and learning in Australia and abroad.

Through this article, Sankey discusses how universities need to update their assessment models to align with how generative AI is being used in professional fields students will enter after graduation.

What do you think are the primary challenges universities encounter with the increase of generative AI, particularly regarding academic integrity and the prevention of cheating?

Universities need to change the assessment model they are using. Valuation should demonstrate the outcomes of learning and prepare students for professional employment, whether in accounting, engineering, healthcare or other fields. Since generative AI is already being used in many professions, part of our responsibility as educators is to ensure graduates are fully conversant with these technologies as they encounter them in the workforce.

This means that educators must first understand how generative AI is being used in their sectors by engaging with the industry. Curricula can then be focused on generating outcomes that mirror real-world applications. The approach to teaching and learning should acquire the skills needed for career readiness, not just retraining students to learn.

"Generative Ai Can Provide Thebaseline Knowledge; Students Mustuse It To Be Productive. By Trainingstudents On These Tools To Launchinto More Fruitful Futures, Wegenerate Graduates Who Can Bemore Productive From Day One,Exactly What Employers Want"

Students should be able to apply the knowledge and not merely demonstrate an understanding of a topic. Generative AI can provide the baseline knowledge; students must use it to be productive. By training students on these tools to launch into more fruitful futures, we generate graduates who can be more productive from day one, exactly what employers want. If assessments expect students to use generative AI in career-focused applications, we better equip graduates for professional success.

What do you think are the recent technological advancements that are helping educators to adapt themselves to generative AI?

New advancements in generative AI for education are emerging rapidly. Each week brings tools with novel affordances for higher education, though it takes time to determine how best to utilize them. Even this week, systems like Gemini 1.5 Pro exhibit astonishing abilities for multimodal content reproduction and interrogation.

Navigating all these AI advancements poses a challenge for educators. Most spend their time immersed with students and have minimal bandwidth to proactively research AI applications. Successful implementation requires a village of academic support staff, IT specialists, instructional designers and leadership to take an integrated approach–no one group can shoulder the adaptation alone. Educators and students must also partner together as mutual learners about how AI can transform curriculum and assessment.

Many students actively use generative AI, while others have not used the technology at all, intensifying equity concerns. Institutions must recognize students–whether school leavers or mature learners–will enter higher education with widely divergent exposures. Academic staff, teaching unchanged for decades, require supportive professional development to incorporate generative AI appropriately. They must grasp the affordances and curricular implications while feeling at ease with unfamiliar tech. This adaptation applies not only to higher education but stretches equally to vocational programs and secondary schools.

A comprehensive, equity-focused support system enables an institution to uplift all its instructors’ readiness. With the right people reinforcing them, today’s educators can remain responsive and relevant amidst rapidly evolving generative AI.

With generative AI, plagiarism is a very well-knownchallenge, how do you think this can be avoided?

Plagiarism arises when students copy-paste existing work as their own. AI plagiarism detectors like Turnitin catch this reasonably well but remain imperfect solutions. Ultimately, plagiarism mainly occurs when assessments simply rehash published information from the past rather than requiring original future-oriented thinking.

We must reframe the assessment accordingly. Rather than reciting historical knowledge, students should utilize generative AI to establish a baseline understanding and then demonstrate how they extend and advance that foundation with their own inferences about potential futures. While AI can synthesize information stored in its training database, truly new perspectives on forthcoming developments generally remain beyond its horizon.

Educators must, therefore, thoroughly test assignment prompts themselves in generative AI to ensure the technology does not inadvertently pre-empt student responses. An alternative lies in program-wide, longitudinal assessments spanning multiple courses and years. These trace conceptual threads and skill accrual over time rather than discrete content. E-portfolios enable tracking student growth and reflective final capstone syntheses of cumulative learning applicable to careers ahead.

Such culminating evaluations emphasize learners connecting their own assessed journey to aspirations beyond graduation. By then, it matters less whether ChatGPT or other AI played an interim role, as students showcase individual leadership ready to take on their own futures enriched by a personalized, integrated educational experience.

Looking ahead 5 to 10 years, how do you see universities adapting their approaches to generative AI to fully reap the benefits of this technology?

In the coming years, we will have a new generation of academics who evolved alongside generative AI. Currently, many professors view the technology as unfamiliar. Soon, however, it will become second nature for instructors. Already in teacher training programs, we introduce emerging teachers to integrating generative AI into their pedagogy. In the master’s course, I’m teaching this term for educators, understanding and applying AI comprises a key curriculum topic.

For this evolution to unfold, institutions must provide opportunities for faculty to actively engage with AI tools. Most universities license the Microsoft suite, including Copilot, enabling everyday integration into workstreams. Like the engineering and accounting firms adopting these technologies, we must do the same for our own staff. 

Over the next decade, consistent exposure will render academics fully conversant in leveraging generative AI across teaching and research. Fluency with technology springs from literacy–when institutions prioritize developing staff AI literacy, faculty can then cultivate students’ literacy in turn. Just as professionals outside academia are assimilating generative AI into their practices, a parallel process will equip the next generation of professors to make AI a natural, value-adding component of higher education.

What advice would you like to give your peers who aim to excel in this field?

Rather than tackling this alone, have conversations with fellow academics and especially industry partners. Professors should understand how alumni apply generative AI in their jobs after graduation. Our textbooks were published before these capabilities emerged and the curriculum must catch up to the pace of change.

Outreach cannot flow one way–academics alone reaching out to business. Industry must reciprocate continuous collaboration so we can properly equip students for their future roles.A two-way street enables institutions to deliver responsive training while employers shape what skills and mindsets they desire in candidates.

This shared stake in developing talent requires an aligned understanding of how generative AI gets used on the job. By collaborating closely with the same companies that will one day hire graduates, academia can remain responsive to actual workforce needs. Industry partners equally benefit from campus expertise in imparting AI literacy to the next generation. Keeping this symbiotic conversation going enables both sides to advance together.

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