Chapter 4: There’s A Time and Place for Technology

No discussion of learning post-2022 is complete without addressing the issue of human inquiry and transformative technologies. The pairing of learning and technology, though, is not new. Remember, quill pens and paper were “new technology” in learning at one point in history (check out the book Teaching Machines: The History of Personalized Learning by Audrey Watters). To do this issue justice, though, we’ve brought in our resident technoskeptic, Interdisciplinary PhD Student Ryan Drew, who, in addition to being the busiest person we’ve ever met, also teaches courses in Inquiry Studies and AI & Society at UPEI. He brings the best of both worlds (learning and teaching) to understanding the role of technology in the past, the present, and in the future.

Following that, Noah (from the introduction) tackles the what and how of using technology ethically in higher education. Noah is highly technocurious and, having recently graduated with his BSc (Psychology, Honours), currently works in AI and educational development while completing his MEd in Open, Digital, and Distance Education, specializing in Instructional Design.

Technocuriosity and technoskepticism give you two useful habits for approaching the technologies that shape your learning. Curiosity asks, “What could this help me do?” Skepticism asks, “What might it be doing to me—or to my learning?” Together, they encourage you to experiment with technologies without simply accepting the choices, assumptions, and priorities built into them.

Technological awareness, then, isn’t about embracing every new technology or rejecting it. It is about becoming more deliberate in how you use technology and more attentive to what happens when you do. The question is not simply, “Can I use this tool?” but “What happens to my learning when I do?” That question becomes especially important when the technology in front of us can generate ideas, explanations, images, and even entire pieces of writing. And that brings us to artificial intelligence.

WARNING: You may be tempted to skip ahead and go straight to the technocurious “what and how” section, but trust us when we say, understanding the technoskeptical “why” helps to clarify the choices we make.

Inquiry Is Always Technological

Questions may feel as though they form somewhere behind your eyes, in the private little office of your brain. But look more broadly: questions actually take shape between you and the world you are trying to understand, through the tools and techniques that make inquiry possible. I use technology in the broad sense proposed by media theorist Neil Postman (1992): it’s not only hardware and software (e.g., iPhones and whatever we install on their app screens), but any organized means of extending, redirecting, disciplining, or reorganizing human action. That means a pencil, traffic roundabout, search engine, prosthetic foot, mirror, and AI chatbot all count as tech. Some technologies are obvious because, like a hammer, they appear as physical things with texture and weight in our hands. Others are what Postman calls invisible: they cannot be held or readily pointed to, and become so thoroughly embedded in learning every day that they begin to resemble common sense (e.g., grades). My own approach to inquiry developed through regimented music training (I’m a professional percussionist) and now moves between several fields. It also rests on the conviction that an instructor should remain a learner, and that curiosity can be cultivated deliberately.

Let’s start here: whatever your year or field of study, start to notice the technologies you already inhabit rather than imagining technology only as the future-tense stuff of cartoons: alien, space-age gadgets beamed onto Earth. Inquirers are persistent engineers of tools. Honestly, I do not think we can help ourselves.

Think about it this way: when you engage with a technology, you are still you, but you are also part of a socio-technical system, which, for the sake of clarity, I will call the machine. I like the example of my grandfather and his lawnmower. You can buy the thing at Canadian Tire, but when the tool is in front of you and running, you become the lawnmower. And dare I say, I-as-lawnmower am a very different machine than my grandfather-as-lawnmower: he has a knack for efficiency, an eye for what the well-kept lawn ought to look like, and a whole set of lawnmower attributes I definitely don’t possess. I carry the baggage of who I am into the machine. If you can see where this is going, the large language model you use is also a different system when you use it than when your grandfather uses it. The you↔ChatGPT machine, to use one timely example, is one of a kind. Anthropologists have made a version of this argument at a larger scale: Edwin Hutchins (1995), studying navigation aboard a naval vessel, located the work of bringing a ship into harbour not inside any one navigator’s head but across a system of charts and instruments and their operators. Makes sense, right?

As a way of learning, inquiry-based learning emphasizes curiosity, agency, and, as a result, ownership. When you have ownership of something—whether you are writing a song or an essay, baking cookies, or designing a house—you are more closely involved in the choices you make and the process you follow. But none of those activities happen in isolation. They all take place within an environment that has already been shaped by other people. The spaces we learn in, the tools we use, the rules we follow, and the expectations we bring with us all influence what we do. Learning works the same way. The questions we ask don’t simply appear out of nowhere. They are shaped by where we are, what we have experienced, what resources are available to us, and what we have been taught to notice. This means that, although inquiry-based learning gives you more ownership over your learning, that ownership still always exists within a particular context. There really is no such thing as asking a question in a vacuum—although if there were, I suspect your tuition would still be high.

I am a learner employed as a teacher, and so I see questions asked in classrooms that are fenced in by disciplines, motivated by deadlines and assessment structures, made possible by language systems, and shaped in more ways than I can fully know by personal histories. The practical point is NOT that you are powerless within these systems but that your agency as and ownership of being an inquirer grows alongside certain kinds of literacy: the ability to read, question, and work within the conditions shaping inquiry. You ARE the part of the machine with the greatest capacity for reflection and redesign. In return, becoming a more discerning and more agile learner means becoming more aware of the technologies that shape how you think, learn, notice, record, test, and create.

Learning Music as a Training Ground for Technological Awareness

Let me give you an example from my own life. I look back to when I was an undergraduate music student at UPEI as a case study for how conservatory-style music teaching reshapes perception and practice.

In the percussion studio, technology is, at first glance, the electric drum set in the corner. But it is also the mirror placed beside the kit, the metronome app (which I ought to have used more), and the mallets chosen for a particular sound: yarn, felt, wood, plastic, brass, and so on. And, of course, technology is an apt category for instruments I now have a degree in hitting. These examples show technology operating in tandem with mind and body. Let’s stick with these three Ms: mirror, metronome, mallets.

• The mirror expands my visual input. It lets me see my posture, gesture, tension, asymmetry, or inefficient movement that might otherwise remain unseen, and thus unfelt. Yet it also changes the nature of attention. The musician is at once both performer and observer, body and image, player and adjudicator. The mirror reorganizes the whole practice situation by adding a visual feedback loop.
• The metronome scaffolds time by externalizing pulse, kind of like how your digital or print calendar externalizes memory. The metronome can stabilize practice, reveal inconsistency, and build rhythmic discipline, but it can also narrow musical attention if treated as the only authority on time. Anyone who has practiced with a metronome knows the little machine is both helpful and tyrannical.
• Mallets extend physical and textural possibilities. They make possible colour, articulation, volume, and expressive range. A phrase played with yarn mallets is not the same musical event as one played with wood or brass. The musical idea is not floating above the material world waiting to be expressed by any available object; rather, it is partly produced through the material conditions of performance.

You may have heard Canadian Marshall McLuhan’s phrase “the medium is the message” (1964), made popular beyond the nerds of communications scholarship by a 1990s Canadian Heritage Minute on CBC (of which Dr MacKinnon is the only one of us who can claim to have seen it live back in the day!). The point I take from McLuhan is that a medium is not a neutral container for meaning. It gives shape to what can happen inside it.

Every tool comes with something like a grid—a set of rules and expectations that shapes what we can easily do with it. The grid doesn’t necessarily tell us exactly what to think or create, but it can make some possibilities easier to see and others harder to imagine. But these grids are not cages. We can work within them, play with them, stretch them, and sometimes even break their rules. But before we do, it is worth asking why the rules are there and what might happen if we change them.

Consider written music. In the Western music education I received, I learned to read music using the familiar five-line staff. We don’t usually think of musical notation as a technology, but it is a good example of an invisible technology. The staff provides a system for representing pitch, rhythm, and other musical ideas. At the same time, that system was developed within a particular musical tradition. If I want to create or understand music that doesn’t fit neatly within that tradition, I may need to stretch the grid—or perhaps use a different one altogether.

This brings us back to the three Ms: the mirror, the metronome, and the mallets. Each is more than simply a tool. Each brings its own “grid” into the learning environment, shaping what musicians pay attention to, what they practise, and ultimately how they learn.

Zooming out from music to inquiry more generally, you work with tools that organize what becomes possible for you, whatever your field. A lab instrument, spreadsheet, citation manager, archive, camera, whiteboard, field notebook, AI system, or peer discussion protocol (e.g., QFT, RWP/WWP, RISE) each opens certain possibilities while quietly discouraging others. Postman (1992) liked to say that “technology giveth and technology taketh away,” a useful warning against treating tools as simple upgrades. The percussion studio where I once laboured as an undergraduate, and where I now sometimes teach, is full of machines: me + mirror, me + metronome, me + mallets, me + notation, me + instrument. In each case, the machine is the user (me) joined with one or more technologies, producing feedback loops among mind, body, and tool.

Five Suggestions for Technology-Mediated Inquiry

A technology is easiest to see at the moment it stops working, which is unfortunate because nearly all the ones shaping your inquiry are probably working fine. But for argument’s sake, let’s consider the pulse oximeter, the familiar fingertip clip used to estimate blood oxygen in medical settings. During the COVID-19 pandemic, Sjoding and colleagues (2020) compared pulse-oximeter readings with arterial blood measurements. Among readings that appeared reassuring, dangerously low oxygen levels went undetected in 11.7 % of paired measurements from Black patients and 3.6 % of those from White patients. The device appeared to be working, but clinically significant hypoxemia (i.e., low blood oxygen levels which can lead to headache, shortness of breath, rapid heart rate, and confusion) was more likely to remain hidden for one group than the other.

Almost everything I am going to suggest in the five strategies for becoming a more deliberate inquirer in technology-rich conditions you’ll find below is contained in that case:

• A functioning device tends to recede into the background.
• Its performance belongs not to the object alone but to the relation among the instrument, the person being measured, and the conditions under which the measurement occurs.
• The disparity also raised questions about how pulse oximeters had been designed, tested, and validated across different populations. FYI, none of this was entirely new—studies had reported related effects decades earlier (Jubran & Tobin, 1990; Bickler et al., 2005). Warnings were there but not heeded.
• The problem became difficult to ignore only because someone compared a working tool against another form of measurement rather than simply continuing to use it.

So here’s what I recommend:

1. Widen your understanding of technology.

Begin with an audit of the technologies currently shaping your learning. You will probably start with the physical tools: the pen, microscope or digital audio workstation on the desk. But also include the search algorithm that orders what you find, the browser and database fields that make some sources visible and others difficult to retrieve, the citation guide that readily accommodates journal articles but not hallway conversations, and the ethics protocol that was established months ago, which questions you may ask. In a laboratory, add the assay that defines what counts as a result; in a studio, the critique schedule that brings unfinished work into public view.

Maybe these do not immediately register as technologies because their “thingness” is less apparent. They feel instead like the conditions within which inquiry proceeds, and a technology that passes for a condition is especially difficult to question. The point is not to stretch the word technology until it means everything. It is to notice the organized forms, procedures, and systems that shape your action in repeatable ways.

Begin, then, with a plain inventory. Name the tools, formats, rooms, schedules, protocols, and methods in play. Sort them into two piles: those you chose and those that arrived already installed. What you chose can usually be reconsidered, and that is rarely where the interesting trouble sits. What arrived installed may have set the terms before you began. Then ask not only what each technology permits but what it shortens. A technology rarely forbids a line of thought outright. More often, it makes one route so much shorter than the others that you mistake convenience for judgment and take the short route believing you chose it.

2. Study each technology’s biases, affordances, and constraints.

A technology does not have the same meaning or effect for everyone who encounters it. You have probably seen the word affordance (i.e., the qualities or properties of an object or environment that indicate how it can be used) used loosely, as though it named a feature stored inside an object. To James Gibson (1979), it meant something more exact: a relation. For example, a fallen log affords sitting to you and shelter to a salamander, but neither possibility waits inside the log on its own. The affordance emerges from the fit between a thing and a creature with particular capacities. You therefore can’t say whether a tool works well without also taking into account who is using it, for what purpose, and under what conditions. That is the half most of us skip.

Imagine a courtroom transcript. If you were present for the testimony, the words on the page recall gestures, pauses, expressions, and tensions that never entered the actual record. You read the transcript as a prompt. If you were not in the room, the same document becomes your principal account of what occurred, even though it contains only what the microphone captured and the transcriber retained. Nothing about the file has changed. What changed is the reader, and with the reader the affordance. The same asymmetry separates a dataset you collected from one you downloaded. The downloaded dataset may appear cleaner and more self-explanatory precisely because you didn’t witness the omissions, repairs, classifications, and compromises that produced it. Distance can produce an odd kind of artificial confidence.

This is why a single use teaches you so little. A relation cannot be inspected from one side, so create a comparison. Put the same paragraph into three forms: write it by hand, redraw it as a diagram, and record it as a voice memo. Notice what each form makes easier to express, what it leaves out, which evidence it renders legible, and which claims it allows you to defend. Do not ask which version is simply best. Ask what each version does to the material. The differences between them are your data and show you how you are best able to express your learning.

3. Trace the wider webs: people, planet, and profit.

To better understand a technology, trace the people, environmental systems, and profit arrangements that created and sustain it. The pulse oximeter makes the point clearly enough: the reliability of its reading depended partly on whose bodies it could measure accurately and how its performance had been assessed. Every research tool carries a comparable history, even when that history has been smoothed into a clean screen and a large blue button marked “Submit”.

Working with a language model connects you to a data centre drawing electricity and water from a particular region (Crawford, 2021), to a body of information gathered from other people’s writing, and to workers hired to label, sort, and moderate data (Gray & Suri, 2019). What we are looking at here is a black-box system—what makes the interface easy to use is that its infrastructure is hidden from view. The same holds for the reagent in your chemistry laboratory, the license attached to your statistical software, and the device on which you will be trained during a work placement. I suggest looking in three directions:

People: Who produced the material? Who was included or excluded? Whose judgment became part of the default settings?

Planet: Which watershed, electrical grid, minerals, shipping routes, and waste systems sustain the tool?

Profit: Who owns the system, who sets its terms, and whether your attention or data form part of the price?

Harold Innis described a similar pattern when he studied what became known as the staples economy. He observed that regions that depended heavily on exporting resources such as timber, fish, or wheat often became organized around the needs of distant markets. Over time, local jobs, transportation systems, and even political decisions could be shaped by the needs of people and businesses somewhere else (1930; 1951). In other words, these communities often supplied the resources while others made many of the important decisions about how those resources were used.

This idea can help us think about something as contemporary as a community that hosts a data centre. The data centre may be physically located in one place—using local land, electricity, water, and workers—while serving companies and users who may be thousands of kilometres away. Some of the decisions that affect the local community may also be made elsewhere.

Trying to investigate every connection in a system this large would quickly become overwhelming. Instead of simply concluding that “everything is connected,” choose one connection and follow it until you find something concrete. You might identify the company that owns the data centre, find out how much water it uses, examine where its electricity comes from, locate a government contract or permit, or investigate the working conditions of the people who maintain it.

4. Use history as a teacher.

New technologies often seem unprecedented because accounts of their arrival emphasize rupture and obscure continuity with the past. In the 1990s, the year 2000 still carried the promise of a glamorous space age; when it arrived, everyday life looked suspiciously continuous with what had come before (trust us, Dr. MacKinnon was there!). History cannot determine what comes next, but it can supply comparable cases to consider. Such comparisons guard against panic and credulity in equal measure.

Consider the close of the silent-film era. Movie houses in the United States employed something like twenty-two thousand musicians, playing beneath the picture six nights a week. Sound film arrived, and within a few years theatres had dismissed nearly all of them (Kraft, 1996). The American Federation of Musicians (the union I belong to today) placed advertisements against what it called “canned music,” and lost. The musicians’ alarm was not misplaced; thousands of jobs were lost. But replacement is only part of the story, because musical labour did not disappear. Recorded sound redistributed it, moving work from thousands of theatres into a much smaller number of studios. In doing so, it changed the structure of the industry, the skills a musician needed, and the conditions under which a profitable career could be sustained. Questions in the rearview, then, concern how technology reorganizes the distribution of work across an industry, including where tasks move, who gains or loses control, and whose knowledge retains value.

Media archaeology (yes, that’s a real and very cool thing!) offers a way to conduct that historical inquiry. It studies obsolete, failed, and residual technologies rather than treating each new tool as the inevitable end of a sequence (Huhtamo & Parikka, 2011). Apply this approach to the community of practice you are preparing to join (Lave & Wenger, 1991), whether radiology, translation, surveying, teaching, or session performance. Choose one of its tools and trace it across a century, paying attention to how the field’s routines, hierarchies, and boundaries changed around it.

5. Make and break technologies that work for you and against you.

Sometimes the best way to understand how a technology shapes what you do is to break it (i.e., use it differently than you normally would). Change one part of your usual routine and notice what happens. What suddenly becomes easier, harder, more noticeable, or even frustrating? That change can reveal ways the technology was influencing your actions or choices that you hadn’t noticed before. For example, in the practice room, moving the metronome from the beat to the offbeats reveals how much of your timing the machine had been carrying. The same principle applies elsewhere. Turn off autocorrect and find out what it has been fixing all this time. Clear the filters that guide a database search. Remove one scaffold and confront what you must now do yourself. Deliberately breaking a tool’s usual operation can reveal the work, judgment, and dependency concealed by ordinary use.

Making provides another route to the same knowledge. Build the modest thing your inquiry needs, whether a note system, data-cleaning script, jig, rehearsal routine, diagram, or prompt structure. Building forces you to account for dependencies that ordinary use leaves to someone else and to discover where the design gives out. A failure you built yourself teaches differently from a critique you merely accepted. Practical command helps you recognize where a tool extends your thinking and where it narrows your choices.

The absurdity of technology-UNmediated inquiry

Suppose you wanted to ask a question with nothing standing between you and the world. Put away the laptop, obviously. Then the duotang, since a ruled page already argues for horizontal rather than vertical ideas. Then the clock, the alphabet, the numerals, and the disciplinary categories learned in your 1000-level courses. Keep subtracting, and you eventually reach language. Vygotsky (1978) treated it as a psychological tool that organizes thought, while Ong (1982) argued that technologies such as writing restructure consciousness. There the exercise stops. Wondering survives, I think—but inquiry does not. Inquiry requires a question to meander, long enough to be revisited, compared, and shared. That’s the fun of it! Those possibilities depend on technologies like a common language or a room where people are expected to disagree. Without them, nobody else can access the thought, and neither can you once the afternoon is over.

To be fair, Plato was thinking about this long before I was. In the Phaedrus (274e–275b), Socrates tells a story in which the god Theuth presents writing to King Thamus as a new technology that will improve human memory. Thamus is skeptical. He worries that if people can write things down, they will rely less on their own memories. Worse, they might appear knowledgeable simply because they have access to written information, without actually understanding it.

It’s a reasonable concern. Writing probably did change how people remembered and understood things, and something may have been lost in the process. But there is a wonderful irony here: we know about this argument against writing because Plato wrote it down. Writing allowed a criticism of writing to survive for more than two thousand years.

The larger point is that we cannot completely step outside technology to examine it. Even when we criticize a particular medium, we must use some medium—writing, speech, video, data, images—to make that criticism. Our understanding is always shaped, at least partly, by the tools and perspectives through which we develop and communicate it.

Donna Haraway (1988) described a related problem as the “god trick”: the temptation to present knowledge as though we can see everything while observing from nowhere in particular. In reality, we always see from somewhere. The questions we ask, the tools we use, the information we collect, and our own position all influence what we are able to know. When researchers leave those things out of their accounts, their conclusions can appear more neutral and independent than they really are.

The goal, then, isn’t to somehow escape these influences. It is to notice them and be clear about how they helped shape what we came to know.

The opposite mistake assigns the tool all the credit or blame. ChatGPT wrote it. Adobe software produced the result. TikTok decided what I saw. Such claims obscure the decisions made by the person using the tool and the influence exerted by the tool itself. Their contributions will not always be equal, but neither can be ignored. Both forms of evasion are now readily available. A person may claim full authorship when pleased with the result, then shift responsibility to the tool when the work is questioned.

The work I hope to prepare readers for is the careful explanation of how an inquiry was conducted and how its methods shaped the result. Depending on the field, this account may appear in a methods section, lab notebook, calibration record, fieldnote, artist’s statement, or elsewhere. Some of these forms make the role of tools and procedures more visible than others. At their best, they record what was used, what those choices made possible, and what they may have limited or changed. This gives readers enough information to understand and assess the work, including the role technology played in producing it.

The Case for Technocuriosity

Technocuriosity: Embracing AI Without Abandoning the Human Mind

You’re now over halfway into Making the Shift from Student to Learner: Reigniting YOUR Curiosity and Inquiry! You know that as you navigate the university landscape, you are not simply memorizing formulas and accumulating facts; you are building the intellectual foundation for lifelong inquiry. In the previous section, you explored the idea of technoskepticism—a healthy caution towards new technologies. It’s natural to feel a bit overwhelmed or wary when groundbreaking tools emerge, especially when they challenge traditional ways of doing things. However, skepticism alone only keeps us on the defensive.

To make the shift from a passive student who merely reacts to tools to an active learner who intentionally masters them, you need to pair technoskepticism with technocuriosity.

Technocuriosity is about approaching new technologies, like Artificial Intelligence (AI), not with blind acceptance or fear, but with an open mind, a critical eye, and a desire to understand how they can genuinely enhance your learning journey. Technocuriosity asks: How does this system actually work? Where can it meaningfully stretch my thinking, and where does it fall short? How can I use it to become a sharper thinker rather than a dependent bystander? Approaching AI with technocuriosity means keeping YOUR intellectual agency at the center of the equation, ensuring that technological tools serve your learning rather than replacing it. This isn’t just about using tools; it’s about becoming a smarter, more effective learner in a world rapidly reshaped by AI.

The Golden Rule: Professor Knows Best

Let’s start with the clearest, most non-negotiable principle: if your professor says “don’t use AI,” then don’t use it. Period.

This isn’t about arbitrary rules; it’s about respecting the learning objectives your instructors have set for their courses. As the Stanford Center for Teaching and Learning points out, AI policies vary dramatically because courses have distinct learning goals (Stanford Teaching Commons, 2023). Some assignments are specifically designed to cultivate skills that AI might shortcut, like critical thinking, original research, or developing your unique writing voice. Your ability to self-assess, reflect, and engage in the writing process is fundamental to your growth as a life-long and life-wide learner (Eaton, 2023).

Always, and I mean always, review your instructor’s course policy and assignment guidelines regarding AI use. This information is typically found in your syllabus (American Psychological Association [APA], 2023). If there’s any ambiguity, or if you’re unsure whether a tool counts as “AI,” ask your instructor directly before you proceed. Transparency is your best friend here. Treat any use of generative AI as analogous to receiving help from another person, and understand that using AI to substantially complete an assignment is almost universally prohibited (Stanford Teaching Commons, 2023). The default assumption should always be that AI is not allowed unless explicitly stated otherwise. Ignoring these directives can lead to serious academic integrity violations, which can have long-lasting consequences for your academic career (International Center for Academic Integrity, 2021, 10; Cotton, Cotton, & Shipway, 2024).

AI as a Learning Amplifier, Not a Shortcut to Bypass Thinking

Now, let’s flip the script. While AI can be misused, it also holds incredible potential as a powerful learning amplifier when wielded correctly. Think of it not as a replacement for your brain, but as an advanced assistant that can help you deepen your understanding and streamline certain aspects of your studies. Remember, critical and creative thinking (challenging though they may be) are where your greatest enjoyment and satisfaction in learning lives!

The key distinction lies in how you use it. Your aim should be to prioritize learning over mere efficiency (EDUCAUSE, 2023). As the USC Center for Generative AI and Society found, most students initially use AI for “executive help”—seeking quick answers with minimal effort. However, the true value of AI for student learning lies in “instrumental help”—using it to clarify concepts, build skills, and support independent learning (EDUCAUSE, 2023).

Imagine learning a new language. You could use an AI translator to instantly convert every sentence, but you wouldn’t actually learn the language yourself, would you? Instead, you might use AI to generate practice sentences, get feedback on your pronunciation, or clarify complex grammar rules after you’ve attempted to understand them. This is the essence of ethical AI use in education: engaging with the material yourself first, and then using AI to enhance that engagement.

The Peril of Plagiarism and Cognitive Offloading: Why Bypassing Learning Harms You

This brings us to a crucial warning: using AI to bypass or avoid your own learning is a profound mistake, even if it feels like a tempting shortcut to a higher grade or to meet a deadline. This phenomenon is often termed cognitive offloading, where you delegate critical thinking processes to the AI (EDUCAUSE, 2023; Risko & Gilbert, 2016).

Why is this so detrimental? Because learning isn’t just about the end product; it’s about the process. It’s about the struggle, the questioning, the synthesis of ideas, the articulation of your own thoughts. When you use AI to generate an essay or solve a complex problem without doing the intellectual heavy lifting yourself, you miss out on developing those essential cognitive skills. You might get a good grade in the short term, but you’re robbing yourself of the very skills university is designed to cultivate: critical thinking, problem-solving, analytical reasoning, and effective communication (EDUCAUSE, 2023).

As Herbert Simon, a founder of cognitive science, wisely stated, “Learning results from what the student does and thinks and only from what the student does and thinks” (Stanford Teaching Commons, 2023). If you’re offloading that doing and thinking to a machine, you’re not truly learning. Studies have shown that students who rely heavily on AI might produce better initial outputs, but they don’t learn more about the topic, nor do they feel more motivated to learn than those who engage with the material directly (EDUCAUSE, 2023). This short-circuits your long-term intellectual development and leaves you unprepared for future academic challenges and professional demands.

Furthermore, using AI to generate content without proper attribution or permission is a form of plagiarism (Eaton, 2021, 10). Even if you paraphrase AI-generated text, if the core ideas and structure aren’t your own, you’re engaging in academic dishonesty. Your university experience is about developing your unique capabilities, not showcasing a machine’s.

Navigating the AI Minefield: Accuracy, Bias, and ‘Hallucinations’

Beyond the ethical considerations, there’s a significant practical risk: AI isn’t infallible. Generative AI tools like ChatGPT, Claude, and Gemini are powerful because they produce text, images, or other content in response to prompts, drawing on vast amounts of training data (Stanford Teaching Commons, 2023). However, they don’t “know” or “understand” in the human sense. They “guess” statistically plausible information based on patterns and relationships in that data (Stanford Teaching Commons, 2023, 17).

This means AI tools can and often do “hallucinate”—they generate information that looks real and accurate, but is entirely incorrect or nonsensical (Stanford Teaching Commons, 2023). This is especially problematic when it comes to academic work:

• Fabricated Citations: AI can confidently provide journal article citations that look legitimate but either don’t exist or misattribute content (Stanford Teaching Commons, 2023, 10). Imagine submitting a research paper with meticulously cited sources, only to find out they are all made up!
• Inaccurate Information: AI can present facts, statistics, or explanations that are subtly or overtly wrong, particularly on nuanced or niche topics (EDUCAUSE, 2023; Cotton, Cotton, & Shipway, 2024).
• Bias: AI models are trained on existing data, which inevitably contains human biases (Stanford Teaching Commons, 2023, 17). This can lead to outputs that reflect societal prejudices, lack diversity, or present a skewed perspective. As a first-year university student, part of your job is to learn to think critically and identify bias in sources, so relying on AI without scrutiny can perpetuate these issues rather than challenging them.
• Outdated Information: AI’s knowledge base is typically limited to its last training update, meaning it may not have access to the most current research, events, or data.

The human-like experience of interacting with AI can be deceptive, making it easy to trust its output implicitly. But remember, you are ultimately responsible for the content you submit (EDUCAUSE, 2023; Cotton, Cotton, & Shipway, 2024; Sullivan, Kelly & McLaughlan, 2023, Perkins, 2023).

Become an AI Fact-Checker: Steps to Verify Information

Given AI’s propensity for inaccuracies and hallucinations, becoming a diligent fact-checker is a crucial aspect of technocuriosity. If you use AI-generated content (with professor permission and proper citation, of course), you must verify everything. Here’s how:

1. Cross-Reference with Credible Sources: Never take AI’s word as gospel. If AI provides a fact, statistic, or explanation, immediately search for that information using traditional academic resources like scholarly journals, reputable textbooks, and established research databases. Perplexity AI (Mollick & Mollick, 2023) is a good tool for this, as it actively searches the internet and provides numbered citations directly to original academic sources, allowing you to verify every claim.
2. Verify Citations and References: If AI generates a citation, don’t just copy it. Look up every single source it provides. Does the article exist? Is the author correct? Is the information cited truly present in that article? Many AI tools are notorious for making up citations.
3. Check for Consistency and Logic: Does the information make sense within the broader context of your course material? Does it contradict other established facts?
4. Identify Potential Biases: Consider the perspective AI might be taking. Does it seem to favor one viewpoint over others? Does it overlook important demographic or cultural nuances? (EDUCAUSE, 20237)
5. Seek Human Expertise: If you’re unsure, consult your professor, a librarian, or a subject matter expert. Their human intelligence and nuanced understanding are invaluable.

This critical evaluation process isn’t a burden; it’s a fundamental part of developing your academic integrity and becoming a discerning learner.

Smart Use: Concrete Examples of AI to Enhance Your Learning

With these guidelines in mind, how can you responsibly integrate AI into your university studies to genuinely improve your learning without cheating? Here are some concrete examples, keeping in mind that you should always clear their use with your professor and cite appropriately:

1. The Active Recall and Self-Testing Partner

Cognitive psychology demonstrates that retrieving information from memory is far more effective for long-term retention than passive re-reading (Roediger & Butler, 2011). You can prompt an AI platform to quiz you on your course readings:

“I have just completed a chapter on synaptic transmission in introductory psychology. Please ask me five conceptual, short-answer questions one at a time. Wait for my response before giving me the next question, critique my accuracy, and identify any gaps in my understanding.”

This turns the software into a dynamic flashcard system that demands mental effort from you before providing feedback.

2. The Socratic Study Partner

When struggling with complex theoretical material, use AI to unpack abstract relationships after you have attempted an initial reading:

“I am reading Kant’s categorical imperative and I am struggling to understand the difference between a maxim and a universal law. Can you explain this distinction using a concrete, everyday analogy, and then ask me a follow-up question to see if I grasped it?”

3. Structural Brainstorming and Thematic Scaffolding

When facing a blank document, use AI as an idea-generating springboard rather than a ghostwriter:

“I am writing an argumentative essay on the ethical implications of urban surveillance cameras. Suggest four distinct perspectives on this issue (e.g., civil liberties, public safety, algorithmic bias, urban planning) to help me plan my preliminary library research.”

Notice the boundary: the tool provides conceptual angles to orient your scholarly library search, but the thesis development, textual synthesis, and prose drafting remain exclusively your own.

4. Code Diagnostics and Debugging

For students learning programming languages, AI can act as an instructional teaching assistant:

“Here is a Python script I wrote to clean this dataset. It throws an IndexError at line 14. Do not rewrite the code for me; explain what typically causes this error in this context so I can fix it myself.”

The Ethical Compass: Transparency and Attribution

Curiosity is a wonderful superpower, but without accountability, it stops being genuine scholarship! As you experiment with these fascinating tools, you want to ground your learning in two simple, rock-solid habits: early clearance and transparent citation.

First, get comfortable talking to your professors proactively. Don’t try to guess what is allowed, and definitely don’t sneak around hoping nobody notices. If you want to use an AI platform to build active-recall quizzes from your readings or help you brainstorm search terms for the library catalogue, just pop into office hours or send a quick, courteous email:

“Hi Professor, I am preparing for our upcoming midterm and would love to use a large language model to generate active-recall practice questions from my own lecture notes. Would this be acceptable within your course guidelines?”

See how painless that is? Reaching out like this shows incredible academic maturity. It respects your instructor’s course design, eliminates guesswork, and saves you from a high-stress academic integrity misunderstanding later on.

Second, if you do have permission to use AI in your work, own it with complete transparency! Never try to pass off synthetic text or computer-generated outlines as your own spontaneous brilliance. The Publication Manual of the American Psychological Association (7th ed.) gives us a very clear blueprint for citing generative AI: treat the company or creator as the author, the model name as the title, and note the specific version and URL (APA, 2020, 2023).

Here is what that looks like in practice:

• In-text parenthetical citation: (OpenAI, 2023)
• In-text narrative citation: OpenAI (2023)
• Reference list entry:
  OpenAI. (2023). ChatGPT (Mar 14 version) [Large language model]. https://chat.openai.com/chat

If you really want to impress your instructors, go one step beyond the bibliography: attach a short methodology note or an appendix at the end of your assignment. Briefly list the exact prompts you entered, the tool you used, and how you integrated the results—whether that was brainstorming broad topics, checking sentence flow, or testing yourself on definitions. When you lay all your cards on the table, you never have to look over your shoulder, and you show everyone that you are the one in the driver’s seat of your education.

The landscape of AI in education is constantly evolving, and your instructors are also learning how to integrate it effectively (Bates, 2023). By being transparent and communicative, you contribute to a culture of responsible AI use and set yourself up for academic success. Embrace technocuriosity—not as an excuse to avoid learning—but as a strategic mindset to enhance your intellectual journey and become a more capable, critical, and ethical learner in the AI era. You’re here to learn, to question, and to grow; AI can be a powerful partner in that adventure, but it’s your journey to steer.

A Broader Vision of Becoming a More Deliberate Technology-aware Learner

Inquiry is always technological. Questions develop through languages, methods, instruments, institutions, and other people. Hutchins’s (1995) account of distributed cognition in naval navigation helps explain how thinking is carried across these relations. Within IBL, ownership includes understanding how a question took shape, how it was pursued, and how the conditions of inquiry influenced what could be noticed, recorded, compared, and claimed.

Consider this: when a door needs instructions, is the problem the user—or the design? A Norman door provides a simple example of how a technology can guide our behaviour. Imagine a door with a large handle that looks as though it should be pulled—but the door actually needs to be pushed. When you instinctively pull it, the mistake isn’t entirely yours. The design of the door encouraged you to take that action.

Research and learning technologies can work in much the same way. A database search box invites you to enter particular kinds of words. A drop-down menu limits you to the categories it provides. A survey asks participants to respond in particular ways. These technologies don’t completely determine what you can do, but their design makes some actions seem obvious or easy while making other possibilities less visible.

Gibson (1979) used the idea of affordances to describe possibilities for action that emerge through the relationship between a person and their environment. Importantly, these possibilities aren’t fixed. People can learn to use tools differently, develop new practices, and sometimes change the systems they inherited. As an inquirer, you will inherit methods, tools, and routines from your field—but you can also test them, adapt them, and sometimes redirect them as your questions develop.