Walk into any faculty meeting or office today and the conversation inevitably turns toward artificial intelligence (AI). Some instructors are experimenting enthusiastically, while others are cautious and perhaps even resistant. Most, however, are simply trying to figure out how to respond to this potential paradigm shift without losing what makes their teaching meaningful.
In the early iterations of AI use in teaching, a familiar pattern has emerged. Faculty are using AI to:
Summarize text readings
These uses are helpful. They mitigate those realities which all faculty are in consistent tension with, including time and routine administrative tasks. However, these gains in improved workflow rarely equate to change in the learning experience for students.
This pattern isn’t new. Educational technologies have a long history of improving efficiency without transforming instruction at a reformative level. In many classrooms, technology enhances the product; the quizzes, the worksheet, the presentation, but leaves the process of learning for the students unchanged. Generative AI has the potential to change this reality, but only if we approach it creatively and from a different perspective. The guiding question in this lens should not be “How can I use AI?” but rather, “How can AI help me create a transformative, student-centered learning experience?”
The Efficiency Trap (and why it is so common)
Consider a simple example. You used to write quizzes by hand, or from a provided resource. Now, AI generates them in seconds. The workflow is faster, but the instructional model is exactly the same.
Decades of research on technology integration show that many innovations end up sustaining existing practices rather than transforming them (Cuban, 2018; De Leon, Martinez, Diaz, & Whitacre, 2019; Tondeur, Van Braak, Ertmer, & Ottenbreit-Leftwich, 2017). This is not because instructors implicitly resist change. Teaching is complex, and new tools must fit within time constraints, institutional expectations, and deeply held pedagogical beliefs. Additionally, staff development is required for most teachers to understand and integrate an innovation that simply did not exist for major portions of their career.
AI often initially enters our classrooms as a productivity tool. But the genuine worth lies in how it can support learning for all students and find those young minds on the margins of our classroom. How can AI help teachers generate and develop student-centered realities which empower our students while simultaneously engaging them? To get to this reality, we need to start with pedagogy.
A Helpful Lens: Pedagogy First, Technology Second
One of the most useful reminders when working with instructional technology is simple: Don’t confuse integrating technology with student use of devices. Focus on the pedagogy. This idea is central to the TPACK Framework (Koehler & Mishra, 2009), which suggests that transformative instruction happens at the intersection of three forms of teacher knowledge: Content, Pedagogy, and Technology. Technology only adds value when it supports and reinforces sound instructional goals, not when treated as a stand-alone “add-on”.
Similarly, the SAMR Model (Puentedura, 2012) offers a practical way to reflect on how technology is being used. At the most introductory levels of this model, technology simply substitutes for existing practices. At its most reformative, it enables learning experiences that were previously not possible.
Neither framework requires instructors to become theorists. Instead, they offer a simple reflective question: Is technology making learning different, or just more efficient? With that lens as our focus, here are three practical ways to use AI to support student-centered learning experiences.
1. Use AI to Differentiate, Not Just Generate
One of AI’s most immediate strengths is the ability to adapt learning materials for diverse learners. Instead of uniformly assigning the same reading or task to each student, instructors can use AI platforms to:
Adapt and “level” complex texts to meet student reading ability Create podcasts, visual supports, and video summaries of a reading Provide vocabulary supports within a reading Generate alternative explanations and points of view Create multiple, stratified versions of an assignment
Imagine assigning a dense, scholarly reading. With minimal effort, AI could allow you to offer a version which has been “text-leveled”, with key terms defined, or even offer an AI-generated podcast or video which offers a concise summary, as an entry point for the students. AI powered tools support this type of differentiation and give the teacher the ability to choose modalities, reading levels and vocabulary tiers which appropriately suit the needs of their diverse learners within the same classroom. Simultaneous differentiation within the same contextual reality.
Try This:
Start with one reading per week. Use AI platforms to create an alternative version such as leveled text, podcasts, or video summaries and offer them as options. Let students choose what helps them learn the material best. The result is not just efficiency; it is increased access and equity.
2. Turn AI into a Thinking Partner
A common fear is that AI will replace student thinking, especially at the critical level. That risk is real, but it depends entirely on how assignments are designed and modeled. When students use AI to produce “final answers”, learning often stops. When they use AI as a dialogue partner, learning can deepen.
Examples include:
History students interview an AI version of a historical figure, offering points of view on contemporary issues based in historical context Business students debate within an AI-generated ethical dilemma Education students analyzing complex classroom realities through AI-created classroom case studies or student profiles Science students testing hypotheses by questioning an AI “Lab Assistant” Students “choosing an adventure” to discover environmental standards
Utilizing interactive AI spaces and simulations can support student engagement and inquiry, supporting student-centered pedagogy by prompting analysis, reflection, and critique rather than simply “replacing” thinking.
Try This:
Require students to submit a short reflection explaining:
What AI got wrong or oversimplified How the interaction shaped their point of view
Now, AI becomes a catalyst for critical thinking, not a shortcut around it.
3. Use AI to Design Better Learning Experiences
Many instructors use AI to generate materials. Fewer use it to design learning experiences, and this may be where the greatest value lies for student-centered realities. AI can help instructors:
Generate discussion questions at multiple cognitive levels Create real-world scenarios for problem-based learning Create layered formative assessments Explore alternative activity formats
The key is keeping student needs at the center of your design.
Try This:
In your lesson planning, ask AI to “give me three ways students can explore this concept through discussion, collaboration, or problem-solving”. Adapt the output to fit your contextual reality, expanding instructional possibilities.
Technology transforms instruction when content, pedagogy, and tools align. Finding that balance takes time, awareness, and intentional practice. No single AI platform will change instruction on its own, but used thoughtfully, these tools can support more meaningful and authentic learning experiences.
AI is already changing the nature of academic work. It can write, summarize, and analyze with remarkable speed. But its most important contribution may be what it gives back to instructors, time to: design richer learning experiences, think more creatively about their teaching, and finding the students. In the end, teaching has never been about the tools. It is about the people in the room. AI can give us an opportunity to return to the core of our profession: listening more closely, responding more thoughtfully, and meeting students where they are. That, ultimately, may be the most human use of artificial intelligence.
Dr. Richard J. Violanti is an assistant professor in the College of Education at Niagara University. He holds a PhD in Curriculum, Instruction, and the Science of Learning from SUNY Buffalo, where his research examined how teachers integrate technology using the TPACK and SAMR Frameworks.
Before joining higher education, he spent more than 31 years as a secondary social studies teacher, department chair, and instructional technology leader in New York State public schools. During that time, he led curriculum redesign initiatives, supported district technology adoption, and facilitated professional learning communities focused on instructional design and ethical technology use.
At Niagara University, Dr. Violanti teaches foundations and pedagogy courses and works to integrate AI literacy and student-centered technology practices into teacher preparation. His work focuses on helping instructors use emerging technologies in ways that strengthen relationships, deepen learning, and support all students.
References
Cuban. (2018). The flight of a butterfly or the path of a bullet? Cambridge, MA: Harvard Education Press.
De Leon, L., Martinez, J., Diaz, Z., & Whitacre, M. (2019). Transformation or resistance? A case study of pre-service teachers engaged in technology integration. Society for Information Technology & Teacher Education International Conference, 1785-1792.
Jandric, P., & Cuban, L. (2015). The dubious promise of educational technologies: Historical patterns and future challenges. E-Learning and Digital Media, 12(3-4), 425-439.
Koehler, M., & Mishra, P. (2009). What is technological pedagogical content knowledge (TPACK)?. Contemporary issues in technology and teacher education, 9(1), 60-70.
Puentedura, R. P. (2012, August). The SAMR Model: Six Exemplars. Retrieved from https://www.hippasus.com/rrpweblog/archives/2012/08/14/SAMR_SixExemplars.pdf
Tondeur, J., Van Braak, J., Ertmer, P. A., & Ottenbreit-Leftwich, A. (2017). Understanding the relationship between teachers’ pedagogical beliefs and technology use in education: a systematic review of qualitative evidence. Educational Technology Research and Development, 555-575.
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