Plugged in but Disconnected in the Age of AI: A Qualitative Case Study Exploring the Causality Orientations of Gen Alpha Students at a Public High School in North Texas

Why I Did This

The edtech industry is built on a premise: that students born into digital environments take to screen-based learning naturally, and that the job is supplying more and better technology. Nearly every product decision downstream of that premise assumes the student is already on board.

I spent years advising high school students and then building content for a college and career readiness platform, and the premise never matched what I saw. So I went and asked.

The question: How do Generation Alpha students at a public school district in North Texas describe their causality orientations in educational experiences with AI and emerging technologies?

I used causality orientations theory — a branch of self-determination theory that distinguishes autonomous motivation (driven by interest and choice), controlled motivation (driven by external pressure), and impersonal orientation (marked by helplessness and low self-efficacy). It gave me a way to ask not whether students liked technology, but what it was doing to their sense of agency.

Method, Briefly

Six students at two programs within one district: an Early College High School and a career and technical education center. Five ninth graders and one tenth grader. Each sat for a 30-minute semi-structured interview, and each program's students also took part in a focus group.

I bounded the sample by grade cohort rather than birth year. Generational boundaries are analytic conventions and the literature doesn't agree on where Gen Alpha begins. What actually matters for this question is shared educational experience — these students moved through the same instructional sequence, hit the COVID disruption at the same developmental point, and encountered the same technology rollouts at the same time.

Getting access took four months: IRB determination, superintendent approval, a district research application, and a background check, then consent from both parents and students. Campus staff handled recruitment and scheduling so that I never received identifying information about students before the sessions.

One timing note that turned out to matter. Fieldwork ran February through March 2025, in a district where personal device use was still permitted. Texas passed HB 1481 in June 2025, banning personal communication devices in public schools statewide beginning that fall. These conversations captured the last moment before that changed. The same data can't be collected in Texas now.

What Students Said

Technology helps, conditionally — and they can tell the difference

Every participant named specific tools that worked and specific conditions under which they stopped working.

Nova valued platforms that let students approach material differently: interactive review games, explanatory videos. Aeris drew a sharper line, noting that game-based lessons hold attention right up until students focus on the game instead of the content. Atria used an AI tool while she was confused and stopped once her teacher explained the material better — she named the teacher as the reason she quit.

The students are not resistant to technology. They are conditional about it, and the conditions are pedagogical.

Saturation produces disengagement, not engagement

All six participants described a school day dominated by screens, and none described that as good.

Elara, in the focus group: "I feel like they're overusing it... Like you [the teacher] talking—this computer—I'm not doing nothing on this computer. You just sitting here talking. Like, I'ma fall asleep."

Nova described teachers who had settled into post-pandemic habits — assigning online work rather than explaining material. Callisto noted that nothing happens on paper anymore. Sora just wanted something to do with her hands.

The pattern is that technology displaced instruction rather than supporting it, and students noticed the substitution.

Dependency tracks instructional failure

Callisto uses AI most heavily in one specific class — chemistry, taught by an instructor she described as not really doing anything. "I use that for almost all the questions and stuff he asks." Elsewhere she'll photograph homework and let a tool return the answer when she doesn't feel like working. But the concentrated dependency isn't distributed randomly across her schedule. It sits where teaching is weakest.

Aeris used a graphing tool as what he called a crutch in algebra, because his teacher was frequently absent. Sora described AI as a last resort — the thing you turn to when nobody gives you the answer.

AI dependency in these accounts is a symptom, not a cause. Students reach for the tool where instruction has already failed. Any product strategy that treats overuse as a student behavior problem — usage caps, friction, honor prompts — is addressing the wrong variable.

Students know what it's costing them

The most striking thing in the data is that nobody had to explain the risk to these students. They articulated it themselves, unprompted, and with more clarity than most industry commentary.

Sora, in the focus group: "[I'm] concerned because nobody gonna know nothing. We all just gonna be [saying], 'oh, lemme look this up. Lemme look this up.' [When] our kids come ask for help on their homework we gonna be looking dumb!"

Elara: "Sometimes, like in certain subjects, I really want to learn it, but the technology is doing everything for you."

Nova objected on almost philosophical grounds — that the pursuit of knowledge is a human activity, and having a system produce answers means nobody learned anything. Aeris put it plainly: we can access everything, and we know less.

These are thirteen- to sixteen-year-olds describing competence erosion in real time, in their own vocabulary.

The Finding That Changed My Mind

I designed the study expecting to sort students by orientation — this one is autonomous, that one is controlled. Every participant showed all three, depending on context.

Sora said emerging technology made her feel at ease because she'd always have an answer. In a different session, she said nobody was going to know anything. Same student, same study, both true.

That undercuts the framing the industry uses. There is no such thing as a student who is motivated or unmotivated by technology. The same student engages or disengages with the same tool depending on how it's introduced and what support surrounds it — which means the orientation is a property of the implementation, not the learner.

For anyone building products: you are not identifying a user type. You are creating conditions that produce a state.

What I'd Build Differently

The recommendations that came out of this for product teams:

Scaffold rather than substitute. The tools students named as harmful were the ones that completed the task. The ones they defended were the ones that explained something they then did themselves. That's a design distinction, not a philosophical one, and it's testable.

Design against saturation. Every tool in a school day competes with every other tool for the same finite attention. A product that is engaging in isolation can still be the ninth screen a student faces that day. Building in non-screen activity isn't a wellness feature; it's what makes the screen time work.

Treat dependency signals as instructional signals. If usage data shows a student leaning hard on AI in one class and not others, that's diagnostic information about that classroom. Most platforms would read it as a student integrity flag. It's closer to the opposite.

Co-design with students. Every useful distinction in this study came from students, not from my framework. They knew which tools helped, under what conditions, and what it was costing them. That expertise is available and cheap to access, and almost nobody asks.

Limits

Six students, one district, one researcher, self-reported data. Findings are not generalizable and aren't meant to be — the value of a case study is depth in context.

The sample skewed female: five of six participants, despite recruiting for balance. Gender may well shape how students experience technology and motivation, and this study can't speak to that.

I also came to this with a stated position — I work in college access and I care about equity in education — which shapes what I noticed. I kept a reflexive journal and used peer debriefing to check my interpretations, but a reader should weigh the findings knowing the researcher was not neutral.

Where It Stands

Defended and conferred, Ed.D. in Learning and Organizational Change, Baylor University, December 2025. Full dissertation available on request.