AI Tools for Teaching and Learning

KINS 158: Motor Learning and Control

Exploratory, Thought-Provoking, Informative

Reflection on Instructional Change:

For the AI FLC, I utilized the use of language-based generative AI, specifically ChatGPT. I was interested in having utilize the "Brainstorm" function of ChatGPT to help with brainstorming and providing ideas for their group project as part of KINS 158. Additionally, I had students also use ChatGPT as part of the same assignment to help provide an initial paraphrase/summary of a journal article.

Although I did not officially survey my students regarding the implementation of these techniques, students appeared very excited to have the green-light to utilize ChatGPT in their assignment. I think that the utilization for brainstorming was less tangibly helpful for the students but students appeared very happy to have an initial draft of the article summary/paraphrase created by ChatGPT (where they could then add/delete/modify as necessary).

The AI Generated Content and Detetectionfrom CSU Chancellor's AI course were definitely the most influential since we were tasked with investigating and identifying multiple new AI tools. I was relatively new to using AI so some of my own exploration during these modules was very helpful. One of the take-aways I had at the time is that there are so many AI tools available yet many go offline and therefore their utility (and even ethics) may be limited at times. Therefore, I knew that I wanted to create an assignment utilizing ChatGPT as it was one of the OGs and best known to students, so I figured it have the staying power and utility for students to learn how to use.

The CTL observe and analyze session was extremely useful to think about how to modify my AI assignment going forward. My biggest take-way was from engaging with my colleagues and realizing the power of discussing how others are approaching the use of AI in their assignments, which was helpful to help guide modifications for the utilization of AI in my assignments going forward.

Evidence of Instructional Change

Evidence of Instructional Change
Example 1	Example 2	Example 3
Figure 1 Assignment handout created for KINS 158 to overview AI, provide instructions on utilizing AI for assisting with brainstorming and some resources to help with prompting with ChatGPT.	Figure 2 Example of a small group deliverable from KINS 158 of the brainstorming activity that utilized ChatGPT's brainstorming function to assist in completing.	Figure 3 Evidence of completion for the AI Tools for Teaching and Learning Course from the CSU Chancellor's Office

Practice and Reflect Teaching and Learning with AI:

During the Chancellor's AI course we did a few activities including generating images with AI and using AI detection software.

I explored using ChatGPT for image generation. The first thing that I realized is that this function was not available in the free ChatGPT version without logging in. After I logged in, I used the prompt "Create an image in Emily Carr's style of a Canadian professor" and was notified by ChatGPT that it could not use Emily Carr's style since it was protected by content policy. So I modified to just as to create an image of a Canadian professor and the attached image was generated. I tried to modify the image and asked to make the professor look more rugged and it generated a more disheveled looking professor. I tried to modify the image again by asking ChatGPT to make the professor "look more athletic" and was met with "It seems there was an issue generating the modification for the image. If you'd like, I can try again or adjust the request further. Let me know what works best for you!".

A few take-aways for me from this exercise:

Good - 1) it appeared that the openAI tool was protecting Emily Carr's style as this wasn't in the public domain and 2) It was fun to see how a Canadian professor would be portrayed.

Bad - 1) the inherent bias/stereotyping of Chat GPT to create the image of a Canadian professor/professor that was a white male with beard and glasses (since I didn't ask it to do this), 2) the limitations to continue to modify, which may be in part an issue of me not being able to effectively prompt or limitations of the system; this made me a bit frustrated because I didn't think "make more athletic looking" a bad or difficult prompt to modify the image.

I think this is a tool that can be used in classes as long as credit it applied to ChatGPT for the original image, even if someone modifies the image further. It would be unethical for someone to take credit for it of course.

Obviously, I come away from this exercise with mixed feelings. I can definitely see the potential use and "fun" with doing this but also acknowledge the biases/stereotyping that can be harmful, limitations of the prompter and/or tool as well as plagiarism that can stem from even generating a simple image without giving proper credit.

I also put some AI generated images through Illuminarty's AI Image Detector and Optic AI or Not Detector. Illuminarty gave probabilities of 39.5% and 33.2% while Optic AI or Not suggested the first image was likely human (attached) while it suggested the second image was likely AI.

A few takeaways from this exercise: 1) I think it is obviously troubling that a clear cartoony type image (attached) was not able to be clearly detected as AI generated and 2) there is clearly the need to put suspected AI generated content through multiple different detection tools.

My 2nd takeaway clearly has implications when we are using it as professors/instructors with our students as we should use these tools to aide but not replace our own judgement.

As for next steps in AI, I think a lot of it starts with being open-minded and willing to adapt. Obviously (as highlighted throughout the course) there are many ethical concerns, which are not going to magically go away. However, continuing to try some of the resources highlighted within this course in different context, and discussing their potential use with students and colleagues will be critical in implementing AI in an effective and ethical manner.

Course Syllabus

KINS158Syllabus_Spring2025_v3.pdf

Faculty Bio

Faculty Bio
Faculty Picture	Faculty Professional Background
	I am an Associate Professor (tenured) in the Department of Kinesiology at Sacramento State. I obtained my BSc (Hons.) in 2008 and MSc in 2010 in Kinesiology and Physical Education from Wilfrid Laurier University in Waterloo, Ontario, Canada. My BSc and MSc research was focused on the effects of dopamine medication treatments on upper limb movement and motor symptoms in individual's with Parkinson's disease (PD). I received my PhD in Kinesiology specializing in Neuroscience in 2015 from the University of Waterloo in Waterloo, Ontario, Canada. My PhD thesis was focused on using electroencephalography (EEG) and transcranial magnetic stimulation (TMS) to investigate how somatosensory information is used in the control for upper limb movements. I completed my training as a post-doc in 2017 at the Krembil Brain Institute at Toronto Western Hospital in Toronto, Ontario, Canada. During my post-doc, I continued my research in investigating how different areas of the brain interact for the control of upper limb movement using a variety of techniques including EEG, TMS, magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) in both healthy and clinical populations. I primarily teach Motor Learning and Control at the undergraduate (KINS 158) and graduate (KINS 258) levels. I have also teach/have taught other graduate level courses, including KINS 209, KINS 211, KINS 294A and KINS 210. I am also the coordinator of the Department of Kinesiology Graduate program. In addition to teaching, I have an active research program in the Sensorimotor Adaptations and Control Neurophysiology (S.A.C. Neurophys) Lab that investigates: Using non-invasive brain recording (i.e. EEG) and brain stimulation (i.e. TMS) techniques to investigate different brain areas and mechanisms that contribute to using sensory information (i.e. somatosensory) for motor control (i.e. upper limb movement); Investigating changes in sensorimotor control in clinical (i.e. Parkinson's disease) and athletic populations; Developing novel techniques (i.e. brain stimulations, exercise) to induce plasticity changes in the nervous system for sensorimotor control; Determining the effectiveness of potential rehabilitation or treatment strategies (i.e. brain stimulation) for clinical populations with sensorimotor deficits.

Funding Source
	This work was supported by: US Department of Education P031S230256 and P031C210012 *Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation or the US Department of Education.