What Four Key Articles Taught Me About AI—and How They’re Changing My Teaching Practice

As I move deeper into my doctoral journey in Educational Technology at CMU, I’m continuously reflecting on how emerging technologies—especially artificial intelligence—shape the online learning environments I design and teach in. Across my DeVry courses (BIS155, BIS310, SEC440), I’ve learned that AI is not simply a tool; it’s becoming a core part of how students access information, regulate their learning, and seek support when they are studying remotely or asynchronously. Below, I share two major ideas from each article and a brief reflection on how those ideas shifted my thinking or informed my teaching practice.

1. AI Hype and Reality in Education (Nemorin et al., 2023)

Nemorin, S., Vlachidis, A., Ayerakwa, H. M., & Andriotis, P. (2023). AI hyped? A horizon scan of discourse on artificial intelligence in education (AIED) and development. Learning, Media and Technology, 48(1), 38–51.
Idea 1: The article highlights how AI in education is often shaped by hype-driven narratives that promise transformative change without adequately addressing equity, bias, or realistic implementation challenges.
Impact on my thinking: This reminded me to evaluate AI tools carefully before integrating them into my remote teaching workflows—especially to ensure that students from all backgrounds have equitable access.
Idea 2: The authors show that AI adoption is often influenced by global economic and political forces rather than actual pedagogical priorities.
Impact on my thinking: This pushed me to stay grounded in learner-centered design and avoid adopting AI tools simply because they are trending or heavily marketed.

2. AI, Upskilling, and the Future of Work (Sofia et al., 2023)

Sofia, M., Fraboni, F., De Angelis, M., Puzzo, G., Giusino, D., & Pietrantoni, L. (2023). The impact of artificial intelligence on workers’ skills: Upskilling and reskilling in organisations. Informing Science: The International Journal of an Emerging Transdiscipline, 26, 39–68.
Idea 1: AI is rapidly reshaping the types of skills employees need, pushing organizations toward continuous upskilling and reskilling efforts.
Impact on my thinking: As an instructor, this made me more deliberate about helping students build adaptable, transferrable skills that will remain relevant in AI-driven workplace environments.
Idea 2: Socio-technical skills—like digital reasoning, communication, and collaboration—are increasingly important.
Impact on my thinking: This reinforced my use of AI-enhanced scenario-based tasks in my courses, giving students hands-on experience applying higher-order skills in realistic situations.

3. AI Literacy for K–12 Learners (Touretzky et al., 2019)

Touretzky, D., Gardner-McCune, C., Martin, F., & Seehorn, D. (2019). Envisioning AI for K-12: What should every child know about AI? Proceedings of the AAAI Conference on Artificial Intelligence, 33(1), 9795–9799.
Idea 1: AI education should begin early and focus on core conceptual understanding, not just programming.
Impact on my thinking: This expanded my approach to teaching AI concepts at the college level by helping students understand the underlying logic of AI before they rely on it as a tool.
Idea 2: Students must understand AI limitations, bias, and fairness.
Impact on my thinking: This changed how I model AI use for remote learners, explicitly demonstrating how to verify AI-generated responses and identify hallucinations.

4. Qualitative Validity and AI Use (Golafshani, 2003)

Golafshani, N. (2003). Understanding reliability and validity in qualitative research. The Qualitative Report, 8(4), 597–607.
Idea 1: Validity and reliability must be reinterpreted through concepts like trustworthiness, credibility, and triangulation when working in qualitative contexts.
Impact on my thinking: This directly informs how I coach students to evaluate AI-generated explanations, especially when they use AI during asynchronous study without direct instructor assistance.
Idea 2: Researcher subjectivity plays an active and essential role in interpreting qualitative data.
Impact on my thinking: This helped me emphasize to students that AI tools are not objective; they reflect training data, embedded assumptions, and human design decisions.

5. Where Are the Educators in AI Research? (Zawacki-Richter et al., 2019)

Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – Where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 1–27.
Idea 1: AI research in higher education is largely conducted by technologists—not educators.
Impact on my thinking: This reinforced the need for practitioner voices like mine to guide how AI is used to support online learners, especially those who depend on AI tools when they lack real-time access to a professor.
Idea 2: AI research tends to emphasize analytics and automation rather than human–AI collaboration or pedagogy.
Impact on my thinking: This made me shift toward using AI as a scaffold and study partner for remote students, helping them reconstruct elements of live instruction even when they cannot attend.

How These Articles Shape My Use of AI for Supporting Remote and Asynchronous Learners

One theme echoed across all five articles is that AI must be guided by pedagogical intention—not by hype, automation, or convenience. As someone who teaches online and hybrid courses where many students cannot always attend live Engageli sessions, AI becomes a powerful companion when used with purpose. Below are a few ways these insights now shape my practice:

AI as a Learning Scaffold for Remote Students

When students cannot attend live demonstrations—especially in courses like BIS155 (Excel/Analysis) or SEC440 (Security Planning & Audit)—AI becomes a structured support tool I can intentionally integrate. For example:
• I provide students with guided prompts they can paste into ChatGPT to recreate step-by-step Excel demonstrations from live sessions.
• I teach students how to ask AI for concept explanations at varying depths (from beginner to advanced).
• I use AI to help students practice skills through simulated scenarios—such as mock security audits or Excel troubleshooting dialogues.
Instead of relying on videos alone, students now have a dynamic tool that adapts to their questions in real time.

AI to Extend Instructor Presence Beyond Live Sessions

Even when I can’t meet with students synchronously, AI tools allow me to extend my presence in meaningful ways:
• I create AI-supported versions of my office hours where students can rehearse questions before meeting with me.
• I design “guided AI study assistants” that help students break down complex tasks (e.g., APA formatting, risk matrix analysis, data cleaning).
• I pair AI feedback with my own targeted clarifications, ensuring students are not left interpreting AI responses without guidance.
This supports students who study during late evenings, weekends, or across time zones.

Building AI Literacy into My Teaching Practice

Inspired by Touretzky et al. (2019) and Zawacki-Richter et al. (2019), I now embed explicit AI literacy instruction into my courses:
• I show students how to verify AI outputs against authoritative sources.
• I teach them how to recognize bias or limitations in AI explanations.
• I integrate activities where students compare AI reasoning to their own problem-solving processes.
This empowers students to use AI critically—not blindly.

Ensuring That Educators Lead AI Adoption

As Zawacki-Richter et al. argue, AI development has largely left educators out of the conversation. My goal as a doctoral student and instructor is to change that by:
• Advocating for responsible AI policies at the course and institutional levels
• Designing AI-supported learning models that prioritize equity and accessibility
• Conducting research on how AI impacts motivation, engagement, and self-regulated learning
Remote students should not be disadvantaged simply because they cannot attend live instruction—and AI gives us the tools to bridge that gap when used thoughtfully.

Conclusion: AI as a Pedagogical Partner—Not a Replacement

Across all five articles, a single theme stands out: AI becomes beneficial only when educators remain central to its design and implementation. Whether we are addressing skill gaps in the workforce, teaching foundational AI concepts to younger learners, or supporting adult students in asynchronous online courses, AI must serve pedagogical goals—not the other way around. As I continue exploring AI in my doctoral work, I am committed to designing learning environments where AI extends support, enhances engagement, and strengthens—rather than replaces—the human relationships at the core of teaching.