Stream 3

Artificial intelligence (AI) has been a buzzword in education for some time now, with the potential to revolutionize how we teach and learn. One of the most promising areas of AI development is generative AI, which uses machine learning to create new content based on patterns and relationships in existing data. This technology has been used to generate everything from music and art to natural language and code.

In education, generative AI has the potential to create personalized and adaptive learning experiences for students. By analyzing data about a student's interests, learning style, and performance, AI can create content that is tailored to their needs. This could include everything from interactive simulations and virtual environments to assessments that adapt to the student's level of understanding.

However, the use of AI in education also raises ethical concerns, such as issues of academic integrity, fairness, and bias in grading and assessment. If AI is used to grade student work, for example, there is a risk that the system may be biased against certain students or groups based on past data.

To ensure that the use of generative AI in education is ethical and responsible, policies and guidelines need to be developed. These policies should address issues such as understanding student learning and outcomes, data sources, privacy, bias and fairness, and the impact on academic integrity. In this proposal, we explore the potential of generative AI in education, its impact on teaching, and the challenges and ethical concerns associated with its use. Attendees will gain a better understanding of the potential of generative AI in education and how to navigate its ethical and practical considerations.
 

I was a Teaching Fellow from 2019 through 2021. My project implemented Team-Based Learning (TBL) in a large, first-year biology course with a process-oriented approach.
I utilized the basic structure for TBL as outlined by the Vanderbilt University Centre for Teaching (2013). The process-oriented approach involved the creation of instructional videos by asking “What came before? What comes after?” In this way, I placed specific content within a greater context. Using this instructional method encouraged students to understand and apply specific concepts in a holistic manner rather than simply memorizing disjointed bits of information. The “What came before? What comes after?” questions, and the process-oriented approach they foster, are applicable to many university courses.

An additional modification to the basic TBL framework was the use of the TBL teams throughout the semester. I used the student -response system Socrative to ask higher-order questions during each lecture. Students sat with their respective teams and worked together to come up with answers. I found this approach strengthened the relationships within each team, enabling the teams to increase their success during the TBL activities.

Quantitative metrics supported the use of TBL with a process-oriented approach. Students scored significantly higher on exam questions dealing with specific content. Qualitative data in the form of general comments and course evaluations also point to this being an effective strategy to promote content mastery and student engagement.

Labour-based grading, sometimes referred to as contract grading, is a type of grading assessment in which grades are based on the amount of labour that is agreed upon between students and the instructor. This grading method focuses on learning as labour, rather than the “quality” of the work that is produced. In the 2022-2023 academic year, Devon Smither (Art History/Museum Studies) and Jodie Asselin (Anthropology) both implemented labour-based grading in their respective classes, including a large first-year survey course and smaller third-year and upper-year seminar courses. In this presentation we will outline the core principles of labour-based grading, examples of how we implemented this assessment format in our courses, discuss the successes and challenges we have faced using this method, and invite some of our students to share their experiences with this assessment schema. This presentation will encourage attendees to reflect on the role that assessment and grading play in their pedagogy and offer an introduction to the ways that labour-based grading can build equity and inclusion in the classroom. There will be ample time for Q&A and the opportunity for attendees to consider how they might apply labour-based grading in their own courses.

1 in every 5 of your students is living with a mental condition; some may be open about what this means for them in the classroom, while others may be completely masking their challenges.

This session is designed to help shed some light on the importance of understanding the impact of teaching on neurodiverse students. From the attention grabbers we use, to the tests we give, neurodiversity can inform all of our practices.

While 20% of the population may experience a mental condition of some sort during their life, young people, ages 15-24, are the most likely age group to experience mental illness and/or substance abuse (Pearson, Janz & Ali, 2013). With this age group encompassing a large portion of university students, it’s important to understand not only what you’re teaching, but who you’re teaching it to.

During this session we will dissect the standard composition of a lesson through a variety of neurodiverse lenses, including anxiety, depression, attention deficit hyperactivity disorder (ADHD), and autism spectrum disorder (ASD). We will examine the introduction, body and closing of a lesson, along with some general assessments, and provide practical strategies and tools that can help support your lesson, as well as the diverse learners in your classroom.