Simon Hodgkins
Are We Preparing Students for a Modern Workplace? with Simon Hodgkins Ep 290 - The Global Discussion
Artificial intelligence is transforming the modern workplace, but are our education systems keeping pace?
In this episode of The Global Discussion, Host Simon Hodgkins explores the increasingly uncomfortable relationship between artificial intelligence, academic integrity, and educational institutions' responsibility to prepare students for their professional futures.
As businesses embrace AI to boost productivity, accelerate research, develop campaigns, and solve complex problems, many students encounter a very different attitude toward the same technology in classrooms and universities.
Simon raises a fundamental question: If employers increasingly value the ability to work effectively with AI, why are some educational institutions still treating its use primarily as something to prevent?
The Growing Disconnect Between Education and Employment
Artificial intelligence is no longer a futuristic concept. It has become an everyday professional tool.
Marketing teams use AI to develop campaigns. Software engineers use it to write and review code. Lawyers use it to analyze documents, while organizations increasingly rely on intelligent systems to interpret data, prepare reports, and automate administrative processes. Employers are looking beyond whether candidates understand artificial intelligence. They increasingly want people who know how to use it effectively.
Yet within education, the conversation can look remarkably different.
Simon discusses growing tensions between students and academic institutions, where restrictions, AI detection systems, and concerns about academic misconduct create uncertainty about what counts as legitimate assistance. The consequences can be significant. Students accused of improper AI use may find themselves defending not only their assignments but potentially their academic records and future careers.
This raises an uncomfortable contradiction: institutions preparing students for employment may penalize them for using technology their future employers increasingly expect them to understand.
Where Does AI Assistance End and Academic Cheating Begin?
Simon is clear that academic integrity remains essential. Submitting an entirely AI-generated dissertation under your own name without acquiring the necessary knowledge is fundamentally different from using technology to support your learning. But where should institutions draw the line? Consider a student who writes an essay and then uses AI to improve its structure, identify weaknesses in an argument, or clarify complicated language. Is that cheating?
What about using Grammarly, a spell checker, or an AI assistant to explain a difficult academic concept? What happens when a student whose first language is not English uses technology to improve their grammar?
These questions illustrate why clear, consistent institutional policies are becoming increasingly important. A substantial difference exists between outsourcing your thinking and using technology to improve it. Simon argues that education needs to become much better at recognizing that distinction.
The Problem with AI Detection Software
One particularly concerning development is the growing reliance on AI detection tools to identify potentially machine-generated academic work.
While designed to protect academic standards, these systems can produce false positives, creating situations where students may have to prove their original writing is genuinely their own.
Simon highlights the troubling implications of allowing an algorithm to influence decisions that could affect years of academic work. As he observes, there is something strange about using artificial intelligence to police artificial intelligence, particularly when the consequences of an incorrect assessment can be so serious. The wider issue is whether an atmosphere of suspicion risks undermining trust between students and the institutions that educate them.
Are We Preparing Students for Yesterday’s Workplace?
Education has always evolved alongside technological change. Calculators were once controversial in classrooms. The internet fundamentally changed how students accessed information, and search engines transformed academic research. Artificial intelligence represents another significant shift in how knowledge is produced, analyzed, and communicated.
Simon suggests that expecting students to graduate into an AI-enabled workplace after being discouraged from using AI throughout their education creates an increasingly unsustainable contradiction.
Rather than approaching artificial intelligence primarily as a disciplinary challenge, he believes AI literacy should become a fundamental component of modern education.
Students should learn how to:
• Write effective prompts and communicate with AI systems.
• Critically evaluate AI-generated information.
• Recognize fabricated sources and inaccurate responses.
• Understand data privacy and responsible technology use.
• Identify the limitations of machine-generated answers.
• Recognize when independent human judgment is indispensable.
Knowing how to work intelligently with artificial intelligence is becoming a professional skill in its own right.
Failing to teach that skill may ultimately disadvantage the very students educational institutions are supposed to support.
Rethinking Assessment Without Abandoning Academic Standards
If a student can generate a passable essay in seconds using a freely available AI tool, perhaps the more important question is what that assignment is actually measuring.
Simon challenges educators to reconsider assessment methods rather than relying exclusively on traditional written submissions. He explores alternatives such as oral examinations, practical projects, classroom discussions, and assessments requiring students to explain and defend their reasoning.
Another possibility is allowing AI within certain assignments while asking students to demonstrate how they used it, what information they challenged, and which decisions they ultimately made independently. This approach would shift attention towards the quality of a student’s understanding and judgment rather than simply whether they accessed a particular technology.
Importantly, Simon does not advocate abandoning independent learning.
Students must still develop the ability to write, calculate, reason, and solve problems without artificial assistance. The objective is to ensure those essential capabilities coexist with the ability to use emerging technologies responsibly and effectively.
From Preventing AI Use to Teaching Responsible AI Use
Perhaps the most important argument in this episode is that the debate itself needs to change. Instead of continually asking how educational institutions can stop students from using artificial intelligence, Simon proposes a more constructive question: How can we prepare students to use AI better than the generation before them?
Artificial intelligence is becoming more capable, accessible, and embedded in professional life. Students currently progressing through education will enter workplaces where understanding these systems is increasingly expected. Schools and universities therefore have two responsibilities that should complement, not contradict, each other: maintaining academic integrity and preparing people for the future. If education continues to approach AI primarily through suspicion, restriction, and punishment, it risks teaching students how to avoid being caught using technology rather than how to use it responsibly.
What World Are We Preparing Students For?
Simon closes the episode with a challenge to educators, policymakers, employers, and anyone interested in the future of learning. Artificial intelligence is already reshaping professional life. The question is whether our education systems are prepared to acknowledge that reality.
The future should not be about choosing between human intelligence and artificial intelligence. It should be about developing independent thinkers who can use powerful technologies critically, ethically, and effectively.
As Simon concludes:
“If we’re teaching students and people to succeed in a world without AI, we may already be teaching them for a world that no longer exists.”
The conversation about AI in education needs to move beyond detection and restriction towards understanding, responsibility, and preparation.
Because the ultimate purpose of education is not simply to assess what students know today. It is to equip them with the knowledge, judgment, and capabilities they will need tomorrow.
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