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What are your two top research questions at the moment?

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What data would you need to answer that question?
Do you have that data, or can you get that data by the end of the Fall term?

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Can you make your project smaller or more focused?  If so, what would that look like?

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How does this fit into the big picture of your career or academic trajectory?

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How would you define your problem of practice?

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1) for my Life Design Plan ideas, after the fall, I will have about 300 students’ plans (each student has 2 pre-course plans and 2 post-course plans)

2) This data should be publicly available on UF websites

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What misconceptions do students have about potential career paths in sport management?

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1) Use of AI for transcribing and analyzing students’ Life Design Plans (holistic 5-year career exploration plans rooted in design thinking applied to career development and career construction theory). Students create plans at the beginning and end (pre-post) of a career exploration course - these plans are visual but include recorded audio narrations. Finalizing RQ and if more of an intervention or looking at only pre- or only post-course plans

2) Examining syllabi policies to explore presence of gen AI policies, type of AI policies, language around AI policies (tone and inclusivity?). Could expand to review semester-over-semesters shifts in university-wide prevalence and type of policies or do follow-up surveys with instructors who have policies to learn more about if/how they discuss policies in class and their teaching philosophies (e.g. to explore if there are any similarities in policy type by instructional paradigm)

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Could narrow down dataset for students to certain career paths or just pre or post course plans.

Could narrow down syllabus idea by semester, undergraduate, non-AI certified courses, etc.

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My first idea is more in line with my research line around career readiness and helps me continue to expand my SoTL work

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Realizing just how much I need to learn about the Canvas data described in last session, even just for sampling purposes! Very helpful; hope to learn more (both here and on my own)~

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Students appear reluctant to declare allowable AI use.

Faculty might be assessing known AI work differently.

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Student Learning vs. Faculty Effectiveness

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Student Learning vs. Faculty Effectiveness

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That is, how can AI help with both? Realizing they are two sides of the same issue, but perhaps require different methods, data, sampling, etc. Easily could see this developing into a larger research platform.

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Using AI as a pedagogical practice/tool to engage digital natives for idea development and critical thinking in design

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Using AI as a pedagogical practice/tool to engage digital natives for idea development and critical thinking in design

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New generations learn differently. We need to adapt!

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Need to remind myself a full _census of, say, students, faculty, documents, platforms, etc. is not necessary. Sometimes, a smaller sample or even a close case study can be more informative or insightful.

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Students struggle in assessing and addressing patients with acute emotional distress. Our goal is to triage and refer, but often students and professionals feel ill-prepared.

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readings

assignments

engagement

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1. Are faculty members measurably assessing known AI and human-generating writing differently?

2. Are students reluctant to declare AI even if they are allowed to use tools?

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I probably have too much data (canvas student outcomes, survey, etc). I will likely need help narrowing the scope of my project to keep myself on track!

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Using AI to develop chatbots infused with personality traits for student engagement. Use AI for scenrario testing and prospective thinking

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Enhance critical thinking and improve design, execution and analysis of experiments

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Canvas data and perhaps surveys

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Keep the study focused on business writing students rather than students in general.

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I am working to collect data this fall

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Chat transcripts. Technology Acceptance Model (TAM). class observations/interviews

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#1 - ACCURACY of AI feedback (as determined by content experts) and how accurately students judge that feedback. Can students recognize "good" feedback from "not so good" feedback? / #2 - Student UPTAKE of AI for Spanish, that is, are students using it on their own time? What are the doing? Why?

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Pedagogical practices in design education

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Pedagogical practices in design education

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New design studio practices

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Do student role-plays help them feel better prepared to assess and address patients experiencing emotional distress?

Does having a human or AI peer available during the session feel helpful in managing the situation?

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1. How does using custom GPT promote personalized learning?

2. How does using AI tools in the course assignments enhance students' learning outcomes (knowledge and critical thinking)?

3. How does using AI tools during the class engage students in active learning?

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Noticeable differences in students performance in AI-integrated assignments and perception between in-person and online sections.

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Students' assignment submissions

Pre- and post-surveys

Interviews

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I am having a hard time figuring out how to measure enhanced critical thinking. For the experimental design, I can actually look at experiments, data and analysis to evaluate the results.

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May focus on one assignment and one learning outcome

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I do not have data at the moment. I would have current audiology doctoral students participate. They are the target audience for this tool. If simulations can be developed quickly, then it is feasible. 🤞🏽

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I have survey data over three semesters, but journals in my field are increasingly refusing survey data as the sole source. I have every transcript, so I see us coding them as the main point of analysis and then comparing them to student eval of AI feedback on their surveys.

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It could be by changing to be a more simple role play experience and recognizing if crisis is present or not. Reduce the amount of expected outcomes.

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This hits upon an aspect of audiology care that is overlooked and many active professionals feel uncomfortable with. The goal would be enhancing student access to meaningful and personalized experiences doing “the hard stuff” with lower stakes.

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1. How do student perceptions of AI and their performance on AI-integrated assignments differ between in-person and asynchronous online course sections?

2. What factors inhibit or discourage students from using AI tools in their learning processes?

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For the 1st research questions, I have data collected from 4 terms. I have twice more students in online section, so this can be a limiting factor.

For the 2nd research questions, I do not have any data yet. I am designing survey questions to measure this, and I will implement survey in one of my online courses that have ~400 students - Hopefully, I can easily collect data.

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1) I think mine is already small-sized (data set wise). But I can break it down to two separate research - one focused on students' perception of AI and another focused on students' performance in AI assignment.

2) I am currently developing the survey questions but I think I can create small number of questions that are directly related to research questions.

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Integrating AI into students learning in the most efficient way (which is what I try to achieve by getting answers to these two questions) will empower students through meaningful, applied learning experiences.

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This project is part of my regular teaching and research on environmental education. I integrate AI into my teaching and conduct research on it to help advance the field of environmental education.

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Student documentation. Chatbot outputs. Discussion board texts. Both could be used for a comparison using qualitative methods.

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Comparison of discussion reflection vs LLM Chatbot discussion conversation.

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Like all new technologies, we want to know if it works. Does this work? Is this comparable to the standard teaching modalities, or is this better? If better, this could be a small AI touchpoint to build a bigger educational program and a program of research with implemtation.

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Critical pedagogy, Educational Technology, and Equity Studies

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Critical pedagogy, Educational Technology, and Equity Studies

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How can the integration of AI in postsecondary classrooms be optimized to enhance equitable learning opportunities for all students, while also accurately assessing and developing students' AI literacies to meet future educational and workforce needs?

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How does the integration of AI in postsecondary classrooms advance or limit equitable learning opportunities for students?

What does the integration of AI in postsecondary courses reveal about students’ existing AI literacies and the need for further development?

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I want to examine surveys from courses that explicitly use AI. My goal is to collect data from different course types within my college.

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This aligns with my broader research objectives, which aim to understand the integration of AI into teaching and research, while also considering the importance of literacy development and the support mechanisms required for students.

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To scale down, I may focus on targeting students within my specific program area to facilitate access to AI literacies.

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For student perceptions, I may need to collect data from a survey. I would need to decide on the questions to include.

For learner satisfaction, I could look for a learner satisfaction measurement tool.

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I could limit the survey to only a few questions.

If I use an exam review chatbot, I could have it available for a specific exam rather than all exams, or have it provide a certain type of question as exam review such as multiple choice.

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It's more of a general fitting into my career, in that I think it's important to incorporate AI into my courses to keep them up-to-date, and give students experience with AI.