Being a Non-Tech Person in a Tech-Driven World
Authors: Kimberly Martinez; info@ciddl.org
Artificial intelligence (AI) is everywhere in education. From lesson planning to research support, it often feels like everyone else knows how to use it while you quietly wonder why it does not do what you hoped. I do not identify as tech-savvy, and my early experiences with AI felt more frustrating than helpful. I wanted support, but lacked the language to ask for it.
AI began appearing more frequently in my doctoral work through tasks such as summarizing articles, generating research questions, and drafting academic language. In these moments, I quickly realized that many of the responses were not usable as written. Summaries missed theoretical nuance, proposed research questions lacked alignment with my coursework, and draft language sounded polished but did not reflect my thinking or voice. I could not copy these outputs without substantial revision, interpretation, and correction.
This post shares practical lessons I learned as a doctoral student navigating AI: what did not work, what finally did, and how non-tech users can use AI effectively without feeling overwhelmed. For educators new to AI, CIDDL’s Office Hours: Exploring AI Literacy in Education offers practical examples of how AI tools are being used thoughtfully in real academic contexts.
When AI Didn’t Work the Way I Expected
Much of my early frustration stemmed from vague prompts. I assumed AI would understand my academic context without explicit direction. Doctoral work demands precision, yet my initial questions lacked specificity.
Here are examples of prompts I initially used that did not generate the level of feedback I was hoping for:
- “Help me with my research.”
- “Can you explain this article?”
- “What should I write about for my blog?”
These prompts lacked purpose, audience, and context. They did not specify my academic role, research focus, audience, or purpose. As a result, the responses were often too general to be useful in advancing doctoral thinking or decision-making.
Learning to Give AI Academic Context
My experience improved once I treated AI as a structured support tool rather than a search engine. I began explaining who I am, what I study, and what I needed.
I started providing context about who I am and what I am working on. For example, I shared that I am a doctoral student at the University of Central Florida, that my work focuses on instructional strategies and student support systems, and that I am interested in using technology to improve efficiency and clarity in academic tasks. Clear prompts produced clearer responses.
Examples included:
- “I am a doctoral student at UCF researching instructional strategies for secondary students with EBD. Help me brainstorm blog topics that explore how AI can support teaching and learning.”
- “I am writing a professional blog for graduate students and educators who feel unsure about AI. Revise this paragraph so it is clear, concise, and accessible.”
- “Based on my background as a former EBD teacher, help me refine this research idea and identify potential challenges I should address.”
By adding academic context, intended audience, and purpose, the responses became more focused and aligned with doctoral expectations. CIDDL’s blog Prompt Engineering for Teachers Using Generative AI offers practical guidance on how educators can frame clearer, more purposeful prompts connected to teaching and learning goals.
Keeping the Expert Centered
AI functions as a tool, not an authority. A calculator performs computations, but users interpret results. AI operates similarly. It generates output based on patterns rather than professional judgment or lived experience. I remain responsible for evaluating accuracy, relevance, and application.
AI does not understand classroom context, course expectations, or learner needs. Responsibility for interpretation, evaluation, and application remains with the user. CIDDL emphasizes critical evaluation of AI outputs and human decision-making in technology integration, outlined in its Framework for Responsible AI Integration in PreK-20 Education.
AI as a Study Partner
AI has supported my doctoral work most effectively as a study partner. I use it to practice terminology, generate review questions, and rephrase complex concepts. These uses support preparation rather than replace learning.
AI is one of several technologies I rely on. I use accessibility features such as recording lectures on my phone, replaying explanations, and organizing readings through digital note-taking tools. Framing AI alongside these familiar supports reinforces its role as a tool rather than a replacement for thinking. CIDDL’s Office Hours: Practical AI Applications for Educators and AI Office Hours on IEP development highlight ways AI can support learning tasks while preserving human thinking and judgment.
Limitations of AI
AI presents important limitations. Models reflect biases from the information they were trained on, and they are not always accurate. It can also raise ethical questions about authorship and how information should be used. AI does not understand teaching, learning, or classroom context in the way people do.
AI can provide information, but it cannot decide what is important or appropriate. It is still up to us to think critically and make informed academic decisions.
Looking Ahead
As I continue my doctoral journey, I am becoming more intentional about how I use AI. AI will continue to evolve and shape educational spaces. Future students will likely encounter these tools earlier and more frequently. Educators will need to model thoughtful use grounded in purpose and interpretation.
Research suggests that AI tools can support critical skills and engagement when used intentionally in learning contexts. Their effectiveness depends on clear goals and human judgment rather than reliance on automation alone (Butson, 2024). Studies in educational technology also show AI supports learning most effectively when educators integrate it thoughtfully into instruction instead of relying on it as a standalone solution (Younas, 2025).
For other doctoral students navigating a tech-driven academic environment, the message is simple: you do not need to be a tech expert to use AI effectively. You need clarity about your goals, an understanding of your academic role, and a willingness to experiment with how you communicate your needs.
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