M Graham's EdDTech Blog for BSU

With the recent prevalence of Artificial Intelligence (AI), several educators have expressed their concerns with how students are using it to cheat. Sarles’ (2025) paper on “aigiarism” explains that students can now use generative AI to create work and pass it off as their own. While there are tools out there to detect AI-generated content—such as GPTZero, ZeroGPT, Quillbot’s AI Detector, and even Turnitin—these are not necessarily accurate. According to both MIT Sloan Teaching & Learning Technologies (2026) and University of San Diego Legal Research Center (2025), AI detectors often fail because of a variety of reasons, but most prominently because it offers false positives for students who are neurodivergent or English learners. Cotton, Cotton, and Shipway (2024) provide the same strategies that are found all over the internet and in schools across the country: use a rubric to evaluate student learning, offer specific instructions, explain that AI-generated work is still plagiarism, make students submit drafts of their work before the final product is due, set strict guidelines for AI use in the classroom, and closely monitor student work. These suggestions are inadequate to say the least. Students who really want to cheat will find ways to do it regardless of the boundaries put into place. Some professors at the junior college where I work have gone back to Bluebooks, some have set up every assignment within a locked-down browser, and some are even making the effort to switch entirely to oral arguments for students to demonstrate their learning. These aren’t good solutions to the “AI problem” as one of my senior colleagues refers to it as.  

Allow me to share my experiences with my Review of English and Composition students and their use of AI. If I suspected AI use in previous semesters, I’d usually ask students why they used AI in the first place. A lot of students claimed poor time management as the issue. They had outside-of-class responsibilities—such as work, child care, and athletics—that interfered with their ability to complete at-home tasks. Some students told me they didn’t understand the assignment and often claimed (sometimes wrongly) that they weren’t in class when I explained the instructions or (bafflingly, considering I post literally everything in Canvas in weekly modules) couldn’t find the instructions at all. Some students admitted that they were lazy and didn’t feel like doing the work (which—not much I can do about that one). The other most common excuse I have heard is that students want As on assignments and feel like generative AI can do a better job than they can. I teach writing. Some of these students are not good writers. That’s okay. I tell them that, too. It’s okay if they aren’t a good writer. I don’t care how polished their grammar is or how complex their sentence structure is. I have to grade for those things, but they’re often only a few points (never more than 5 combined points on 50-point assignments). I tell my students that I care about their opinions and ideas. They’re often really surprised by that, and it’s heartbreaking.  

Rather than focusing on forcing students to produce “perfect” final drafts, I have shifted the focus in my classroom to students producing wildly imperfect rough drafts. Yes, I do have one single assignment that they have to polish to the best of their ability because they do still need the skills to do something like that. However, when I introduced my new assignments this semester and asked students to tell me specifically what they thought about a topic (they always had options), I saw the use of generative AI decrease dramatically. Did it still happen? Of course it did. I’d be crazy to think I could eliminate it entirely. Before ChatGPT, Claude, CoPilot, and all the other generative AIs out there, professors had to deal with students paying someone else to write their papers for them or buying a prewritten paper on the internet. Before that, the problem was literal over-the-shoulder cheating. This has never not been an issue. The problem is that we, as educators, have to find new ways to address it. Generative AI is free. It might not be great, but it’s free, and that alone is one of the reasons students use it as much as they do.  

Educators need to change their focus from preventing AI misuse to shifting the types of assignments we require and how we conduct classes. Is this easier for some subjects and harder for others? Absolutely. It still needs to be done. Here’s how I intend to do this (which I began in Fall 2025 and will continue to adjust until I’m satisfied). 

I have several overriding goals of Educational Technology in my teaching-learning environment:  

Equitable, personalized learning to prepare students for the workforce 

Student collaboration to solve problems and design solutions 

Motivation to learn  

Development of digital citizenship skills 

“Equitable, personalized learning to prepare students for the workforce” will mean different things to different educators. It depends on a variety of factors: subject matter, school policies, physical location, socio-economic status of the school and the learners, and more. I teach Composition 1 and 2 at a small junior college in the Permian Basin. I’ll set aside school policies. The school itself is fairly wealthy in terms of what our board is able to do for the faculty, staff, and students, and we have some pretty robust resources available. My students, typically first-year, first-generation college students, are mostly English learners. Something like 45% of all students at the school are Hispanic women with young children. We have a lot of international athletes who are English learners as well. Many of the students who graduate from the college where I teach get their associate’s degree and do not continue to a four-year institution for a variety of reasons. The last statistic I saw was that only 8% of our students go on to pursue more advanced degrees. My focus for my students has to be on what they need to know so they can graduate and either enter or return to the workforce. I need to make my lessons and the tools I use in the classroom equitable for these students with that goal in mind.  

What does it mean to be equitable in education? To me, equity means providing my students with the resources they need to be successful regardless of their socio-economic status. Doing this in education means providing them with various supports and opportunities to reach their full potential as students. I do this by providing digital readings via Perusall, which has a read-aloud function, as well as providing PDFs of the readings for students to download or print as they require. We have several read and annotate assignments that my department mandates, and I accept annotations done on the Perusall platform or as handwritten notes on printed paper. I offer my students any assistance they need to customize their font settings (great for dyslexia) or add a colored overlay to their browser to help them focus (which has been particularly helpful for my self-reported ADHD students). I provide any additional time to complete or submit assignments as required by any accommodations they receive from the school. I also provide those same accommodations to students who ask me for them (in advance). I have a fairly generous late policy, too, while still stressing the importance of time management and personal academic responsibility. I provide mutliple scaffolding assignments to help students build up their final projects (such as research questions, annotated bibliographies, and rough drafts before a final research project). I provide very detailed feedback on all student work. I also offer to provide leveled texts to students who need or want them, always with the understanding that they should rely primarily on the original text, not the leveled version. I mandate time spent with professional tutors and one-on-one meetings with me to discuss rough drafts and improvements I’d like to see for each student. We have a wealth of digital resources available to facilitate equitability for students, and I use them to the best of my ability whenever and wherever I can. Equitability is the most important goal I have for myself as an educator.  

The issues that Educational Technology can and should address in my teaching-learning environment are:  

Accessibility for all students regardless of learning style, ability, or socio-economic background 

Engagement with the material in a way that improves comprehension and retention of the material and encouraging students to learn to apply their knowledge to other problems they encounter (knowledge transfer) 

Personalized learning pace for more difficult or easier concepts depending on student need 

Accessibility goes hand-in-hand with equitability. I strive to provide multiple methods of delivering the materials for my students so they can understand everything they need. I do not think of accessibility as only providing access to a computer in class (though that is something the junior college also does). Accessibility in education includes incorporating aspects of universal design for learning (UDL) in all my lessons, providing online versions of my materials and lessons in addition to paper copies, and accommodations and modifications where required or requested. Most students do perfectly well without these, but for those that need them, it is often a life-saving experience. Many of my students have been grateful for the tips and tricks I’ve shown them how to do on their own. They’ve reported that they began using the same tools for their work and personal lives, not just for school assignments. I strive to ensure the majority of the tools I provide are completely free. If they are not, then they are very low cost and I warn students of that in advance as well as help them seek out alternatives that may not work the same way or as well but which are obtainable because of no cost.  

Engagement and knowledge transfer also work together. Engaging students in material they might not be interested in has always been a challenge for educators. This isn’t a new problem. The trouble now is that students often have a very limited attention span due to social media applications like TikTok and Instagram. The short videos featured on these platforms have led to a massive decline in attention span among students. Attkinson and Shiffrin’s (1968) information processing theory as well as Gagné, Briggs, and Wager’s (1992) cognitive behaviorist theory discuss using technology in part to grab students’ attention when introducing new lessons. By creating technology-based lesson openers (at least) that are interesting and eye-catching, I can use only a little time to grab my students’ attention and get them interested in the material for that day. My focus for next semester specifically is only rhetoric and rhetorical appeals. My plan is to use old advertisements in conjunction with commercials from when I was young as well as current ads on social media websites like TikTok. Asking students to find these advertisements for themselves is another way I can immediately engage them and keep their attention. Once I have their attention, I have to help them focus on the core concepts of the lesson and get them to connect what they are currently learning to things they’ve learned about previously. I want my students to leave my classes with the ability to take what they’ve learned and apply it to new challenges and problems they might encounter in other classes, in real life scenarios, and in the workforce. This is knowledge transfer (Roblyer & Hughes 2019).  

Personalized learning is a concept that is easier to discuss than it is to put into practice. According to Roblyer and Hughes (2019), personalized learning includes three primary features: 1) a student profile, 2) student-controlled learning paths, 3) formative assessment that leads to progression based on competency, and 4) a lot of teacher and school support. The question is: how do we determine what type of learner each student is? In a 16-week course, there isn’t a lot of time to throw several different types of assessments at them and see which they do best on. Educators can ask students to self-assess and report their findings, but many students don’t tend to take that sort of thing seriously. Allowing students to decide on their own learning paths and choose their own goals is great in theory, but many students’ goal is to pass the class and nothing beyond that. For a core subject like composition that every student has to take, it’s inevitable that at least some students aren’t going to be interested in the class. Formative assessments are something most educators do already in one way or another, and many schools and teachers offer plenty of support to their students. Getting the students to ask for that help is another challenge entirely. My goal is to take this concept of personalized learning and modify it into something that works best for my classes and my teaching style. What I do won’t be the same as what any other professor does. That’s okay. I want to provide options for my students. I can provide them with an assignment that can be completed in multiple different ways. The student can then choose which way works best for them all while maintaining academic rigor. This neatly combines the first two concepts of personalized learning. The student can look at the assignment’s requirements and determine for themselves what they feel they will do best with and what interests them the most. They can decide which of the versions of an assignment will best suit them, their learning style, and their ultimate goal for the course. Ideally, I’d do this through gamification, but I’m still a little far out from that right now. (And that, too, is okay.)  

There are many learning theories that drive Educational Technology (Roblyer & Hughes 2019). Below are some of them sorted by category of objectivist or directed instruction and constructivist. Neither is superior over the other, to me, and it depends greatly on the subject matter and skill level of the learner for which theory is used. I tend to use a mixture of many of them for all my lessons and assessments throughout my courses.  

Objectivism/directed instruction 

Behaviorist theories (Skinner) 

Focuses on consistent presentation of stimuli and reinforcement, operant conditioning, observable behaviors that are controlled by consequences, and the idea that behavior can be shaped by reinforcement 

Teaching is all about arranging contingencies of reinforcement to bring about learning 

Bloom used Skinner’s principles  

Drill and practice software was developed based on Skinner’s principles to help students memorize important basic information 

I specifically use drill and practice games to help students memorize definitions and practice grammatical concepts  

These are especially useful for my English learners and those who have not attended school for a long time or who may not be at the level I need them to be  

Information processing theories (Atkinson & Shiffrin) 

Information and lessons must be attention-getting, repetitive, and include individual practice 

There are 3 kinds of memory stores: sensory registers, short-term memory, and long-term memory 

Information that is learned has to progress through that path or it’s lost forever 

Computer programs in general are ideal environments to incorporate the features of information processing theories 

The ideas behind these theories helped to guide the development of AI 

I often use PowerPoints to introduce lessons and explain the concepts my students will explore 

I have students utilize AI when they want instantaneous feedback on assignments as they are working on them, which requires some prerequisite knowledge of how to use AI for that purpose (which I also teach them) 

Cognitive-Behaviorist Theory (Gagné) 

9 events of instruction: gaining attention, informing learners of objectives, stimulating recall of prereq knowledge, presenting new material, providing learner guidance, eliciting performance, providing feedback, assessing performance, and enhancing retention & recall 

Several types of learning behaviors 

There’s a learning heirarchy 

The events of instruction can be used to plan lessons using drill and practice, tutorial, and simulation software 

I use the 9 events of instruction for each new lesson in that exact order while integrating technology and social learning for different events 

Systems Approaches to Instructional Design 

Requires consistent presentation of new information, practice, and assessments 

Need: goals & objectives; decide learning conditions; align assessment with goals & objectives; create materials that deliver learning strategies; test and revise before finalizing  

For example: teachers must set an objective, then develop a sequence of activities 

The system used must be structured & sequential and continually monitor student progress 

Using specific stages of the 9 events of learning to guide students through explicit instruction has been overwhelmingly successful in my classes  

Constructivism 

Social activism theory (Dewey) 

Promotes social interaction among students on problems and issues of direct interest to them 

School curriculum should be pedocentric not scholiocentric  

Education is growth, not an end in and of itself 

Learning needs to be hands-on and experienced based, not abstract 

I often use games and simple small-group or paired activities to have my students practice the concepts we are learning for each lesson 

Social cognitive theory (Bandura) 

Modeling increases student self-efficacy to learn the behaviors being modeled 

Internal cognitive processes shape their actions—they are not only a result of external consequences as Skinner believed  

Students are more likely to imitate what a teacher does instead of listen to what they say 

I model everything for my students each time I’m introducing something new or reteaching a concept they learned previously  

Scaffolding theory (Vygotsky) 

Students learn by building on what they already know 

Cognitive development is linked to social development 

Learning and thinking are derived from culture 

Zone of Proximal Development (ZPD) 

Davydov’s 6 basic implications for education  

I scaffold all assignments rather than providing students with one major project 

Child development theory (Piaget) 

Learner abilities are different at each developmental level 

Children progress through those developmental levels by exploring their environment 

2 features: stages of cognitive development & processes of cognitive functioning 

4 stages of cognitive development: sensorimotor, preoperational, concrete operational, and formal operational  

Disequilibrium can only be resolved through assimilation and accommodation 

Although I teach primarily adults, it’s still important to keep these concepts in mind, as some of my students are still quite young (as young as 12 in one rare exception but often as young as 15 and 16) 

Discovery learning (Bruner) 

Children learn better when they explore concepts  

3 stages of intellectual development: enactive, iconic, and symbolic 

Requires prerequisite knowledge  

I have my students explore the concepts we discuss by searing the internet for examples and sharing those with their peers and discussing what they’ve found and how it relates to the concepts they’re learning to build from inactive to symbolic development  

Multiple intelligences theory (Gardner) 

Learning occurs and is demonstrated in different ways that depend on the learner’s type of intelligence 

8 different types of intelligence: linguistic, musical, logical-mathematical, spatial, bodily kinesthetic, intrapersonal, interpersonal, and naturalist 

Traditional academic tasks aren’t the best reflection of a student’s abilities 

My ultimate goal is to provide different assessments for each of the 8 types of intelligence so my students can choose assignments that best suit their needs and learning styles 

References  

AI Detectors Don’t Work. Here’s What to Do Instead. MIT Sloan Teaching & Learning Technologies. (2026, May 6). https://mitsloanedtech.mit.edu/ai/teach/ai-detectors-dont-work/ 

Atkinson, R., & Shiffrin, R. (1968). Human memory: A proposed system and its control processes. K. Spence and J. Spence (Eds.), The psychology of learning and motivation: Advances in research and theory. New York: Academic Press. 

Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2024). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education & Teaching International, 61(2), 228–239. https://doi-org.ezproxy.nmjc.edu/10.1080/14703297.2023.2190148 

Gagné, R. M., Briggs, L. J., & Wager, W. W. (1992). Principles of instructional design (4. ed). Harcourt Brace Jovanovich College Publishing. 

“Generative AI Detection Tools: The Problems with AI Detectors: False Positives and False Negatives.” University of San Diego Legal Research Center, 2025. https://lawlibguides.sandiego.edu/c.php?g=1443311&p=10721367. 

Roblyer, M. D., and Joan E. Hughes. Integrating Educational Technology into Teaching. 8th ed. Pearson Education, Inc., 2019.