When I first started my graduate program at ASU, I really didn’t like having to use an AI mentor. I wasn’t convinced it would add much value to my learning, and honestly, I didn’t see why I needed it. Over time, though, my perspective changed. I started recognizing that some of the questions it asked pushed me to think more deeply and consider things I might otherwise overlook. Now I actually appreciate having it as part of my learning process.
That experience came to mind as I worked through Module 1 and started thinking about where I fall on Rogers’ Diffusion of Innovations curve. I realized that my relationship with technology isn’t quite as straightforward as I initially thought.
Finding My Place on Rogers’ Curve
At first, I wondered whether I belonged in the Early Majority. I don’t necessarily jump on every new technology that comes along, and I like to understand what something offers before investing time in it. But when I started thinking about how I actually use technology in my professional life, I realized that I do quite a bit of experimenting on my own.
As a teacher in an independent study program, I’m constantly looking for ways to make my work more efficient and improve communication with students and families. I’ve used Claude to create HTML-formatted emails that make instructions easier to follow, whether I’m explaining how to schedule a meeting, submit a work sample, complete an engagement log, or participate in an intervention. I’ve also created websites to support my classes and provide resources for students.
More recently, I’ve used ChatGPT to help align large sets of student assignments with California State Standards. Previously, I would have needed to work through those materials page by page, which was incredibly time-consuming. Using AI has helped me streamline that process while still relying on my own professional judgment to verify the alignments.
What I hadn’t initially considered was how much of this work extends beyond my own students and families. In my role as a School Support Lead, I regularly share resources and solutions with other teachers. I’ve become someone colleagues know they can come to when they need help because chances are I’ve already created something they can use. I genuinely enjoy that part of my work, especially knowing that something I developed can save another teacher time or make their job a little easier.
That made me reconsider where I belong on Rogers’ curve. Early adopters don’t just experiment with innovations themselves. They can also influence adoption by demonstrating their usefulness to others. I see that happening in my own professional community. I think Early Adopter is the best fit for me, although my willingness to experiment depends on the circumstances. I’m comfortable trying new tools to improve communication or streamline my work, but I’m much more cautious when a technology involves student privacy, questionable research, or significant financial investment.
Looking Beyond the Hype
One of the concepts from Module 1 that stood out to me was the difference between a technology’s potential and its actual value. Reich and Dukes (2025) discuss how technologies can enter educational settings before institutions have fully considered how they should be integrated. Their concept of arrival technologies made me think about how quickly AI has become part of education and how educators are still determining where it adds value.
My own experience with the AI mentor is a good example of how perceptions can change. Initially, I didn’t see its usefulness, but through experience, I began recognizing its benefits. At the same time, I don’t think that means every application of AI is automatically valuable. I think that’s part of what makes evaluating emerging technologies challenging. We need to remain open to possibilities without assuming that innovation always leads to improvement.
What I Learned from Beyond the Curve
The Beyond the Curve simulation gave me an opportunity to think more carefully about how I make adoption decisions. I evaluated neuroadaptive attention tracking, a technology that uses EEG headbands to measure students’ attention in real time. I initially placed it in the Innovation Trigger phase of the Gartner Hype Cycle because the technology seemed relatively new and the vendor’s claims were ambitious compared to the supporting evidence. I could see why it might appeal to instructors teaching large lecture courses, where determining student engagement can be challenging. However, I questioned whether measuring brain activity could actually tell us if students were learning.
One of the biggest concerns for me was the vendor’s claim of 94% accuracy. When I looked more closely, the supporting study had only measured whether participants’ eyes were open or closed. That seemed like a pretty significant leap from claiming the technology could measure attention.
I chose to wait rather than immediately invest in a pilot. I also spent $25,000 on an independent accessibility, privacy, and responsible-use review because I was concerned about the ethical implications of collecting students’ biometric data. I kept thinking about students with ADHD or other neurological differences and how easily they could be mislabeled based on information that might not accurately reflect their engagement.
One of the most interesting moments in the simulation came when my AI mentor asked what instructional decision the EEG data could actually support. That question made me reconsider what I had been focusing on. I was so concerned about whether the technology could accurately measure attention that I hadn’t fully considered what an instructor would actually do with that information. Even if the measurements were accurate, would they tell an instructor why a student was struggling or what needed to change?
As an educator, I would much rather have information from formative assessments, interactive polls, or opportunities for students to demonstrate their understanding. Those approaches give me information I can actually use to adjust instruction. I also found it interesting that the same type of AI mentor I initially resisted when I started graduate school was now helping me think more critically about emerging technology.
When Being Cautious Makes Sense
What surprised me about the simulation was how cautious I became compared to how I typically approach technology in my own work. After 18 months, the technology evolved into a less invasive, voluntary wellness tool rather than a classroom monitoring system. I reconsidered my original position and supported a smaller implementation that allowed students to access it as an optional study tool through the university library.
By the end of the 36-month simulation, I had spent $55,000. Faculty trust had increased slightly, and there was no additional ethical debt, but learner outcomes remained unchanged. That made me question whether the investment had been worthwhile. I don’t necessarily think I made a poor decision because I was working with the information available at the time. However, the outcome reinforced that making a technology safer doesn’t automatically make it educationally valuable.
The BUILDS framework also helped me recognize the different factors influencing my decisions. Learning, User Experience, and Impact were the dimensions I considered most naturally. I wanted to know whether the technology would improve learning, how it might affect students, and whether it offered meaningful benefits.
I also considered Dependencies, particularly the need for independent research and appropriate safeguards. However, I realized that Business Value deserved more attention. Spending $55,000 without measurable improvements in learning is something I would need to examine much more carefully in a real organization. Udell and Woodill (2019) emphasize the importance of evaluating emerging technologies in relation to organizational needs. That connected directly to my experience. A technology might be innovative and interesting without necessarily being the right investment.
What I Want to Take Forward
As I continue through LDT 511, I want to become more intentional about how I evaluate emerging technologies. I’m comfortable experimenting with new tools, especially when I can see how they might improve communication, streamline my work, or support students. But I also want to strengthen my ability to evaluate the bigger picture, including financial costs, implementation requirements, accessibility, and long-term impact.
My experience with the AI mentor reminds me that sometimes I need to give a technology a chance before I can recognize its value. My experience with neuroadaptive attention tracking reminds me that sometimes waiting is the better decision. I don’t think being an early adopter means saying yes to every new technology. For me, it means being curious enough to explore what’s possible while remaining willing to question whether it actually makes a difference. Ultimately, I don’t want to adopt technology simply because it’s new. I want to adopt it because it helps solve a meaningful problem.
References
Reich, J., & Dukes, J. (2025). The future of education technology after the arrival of ChatGPT. Phi Delta Kappan, 107(3–4), 19–23.
Udell, C., & Woodill, G. (2019). Shock of the new: The challenge and promise of emerging technology. ATD Press.