# data[].attributes.body
1. LMS (Canvas) is a learning management system.

2. Use of Canvas leads to minimization of email communication about course material with students. It also provides tools (canvas quizzes) for online assessment with some auto-grading functions to minimize TA workload. Additionally, canvas does a nice job facilitating TA grading and providing feedback on assignments.

3.  I think this tool helps in ways of Substitution and Augmentation, making content delivery and administration of assessments easier, but also providing additional tools providing feedback to students. I would like to explore ways of facilitating additional formative assessments (e.g. exit survey/reflections which tend to be difficult to grade with large classes) using Canvas to enhance student learning.

# data[].attributes.body
Last fall we taught out students how to use GenAI such as ChaptGPT to efficiently find archaic fungal identification keys that are a critical part of a major course project. GenAI has really helped streamline and increase the efficiency of finding literature that's relevant for their project. But then the student still has to be able to point to physical characteristics on a mushroom that they collected and then compare that with published literature to build an identification argument. I think the SAMR level for this would be Substitution - we're using ChatGPT now instead of a regular search engine such as Google.

# data[].attributes.body
Technology and Purpose: Video conferencing to host guest speakers who would otherwise be unable to speak to the class.

Impact on Teaching: Allows for students to hear broader thoughts on the topic at hand. Hear from other professionals in the industry and connect readings to practitioner thoughts. Allows lectures to be global.

SAMR Level: Augmentation - the lectures are similar but we have less restrictions on locations of speaker and student. However, the heart and general format is the same.

# data[].attributes.body
Generative AI has had a clear impact on my teaching practice. In my teaching context, students often need timely support, practical examples, and more than one way to review course material. Because of that, I started using AI in a more intentional way.

One way I used it was to revisit my course outline and make it more AI-integrated. This pushed me to think more carefully about where AI can support learning in a meaningful way instead of adding it just for the sake of it. I also created a customized GPT as an office hour helper. It gives students another place to ask questions, review ideas, and get unstuck between class and office hours. This has improved student support and has also helped me notice where students are confused most often.

I also used Socrates AI to create practical and engaging activities. It helps me build examples and tasks that are closer to real situations, which makes class more active and useful for students. Another tool I used is NotebookLM to create educational podcasts from course materials. This gives students another way to review content, especially when they want to listen while commuting or studying.

In the SAMR model, I would place this mostly at the level of Modification, and in some parts close to Redefinition. I am not only replacing an old tool with a new one. I am redesigning parts of the course, the support system, and the way students interact with course content. Going forward, I want to keep using AI carefully, keep my own judgment at the centre, and help students use AI in thoughtful and responsible ways

# data[].attributes.body
I began using Copilot to generate plausible synthetic data for real world cases so that students could apply analytical techniques to situations that mattered to them (as identified in introductory surveys), but where data was otherwise inadequate or unavailable for the types of techniques taught in the course. I always informed the students that the data was synthetic.

This has enabled me to be more inclusive in my teaching by bringing in examples that might not otherwise be possible and that align with student interests.

While this use probably only fits at the level of substitution, it has implications for inclusion insofar as the time needed to create synthetic data manually could preclude the inclusion of relevant examples in the course due to time constraints.

# data[].attributes.body
Technology and purpose: Canvas and Qualtrics for quick quizzes and feedback during/after class.

Impact on teaching: I can use canvas quizzes to do short quizzes on what has just been taught in class and have it automatically grade the answers. This allows me to focus more on lectures and understanding rather than having to constantly grade submissions. It also allows me to judge the participation and understanding levels of students as it happens almost in real-time with teaching. I use Qualtrics forms for feedback after lectures, as it allows the students to give anonymous feedback. I can use this feedback to judge whether I need to revisit any topics or my lecture style.

SAMR level and future use: Augmentation/Redefinition – I plan to have a set of canvas quiz question banks that I can automate to my lecture material, reducing the effort of having to curate every quiz individually. Further down the line, I would like to explore the use of notebook LM to enhance the quiz experience, with relevant lectures uploaded and questions generated from that lecture material.

# data[].attributes.body
Technology & Purpose: Canvas Grading Tool: Last semester (admittedly before I started this module), I had students submit their assignments on Canvas. While I have always done this since I started teaching a year and a half ago, this past semester I used Canvas to create a rubric for each assignment and to break down the points that could be earned in each category. The purpose was to give students a breakdown of the categories they would be graded on and the weight of each category.

Impact on Teaching: I have found that as class sizes have increased, it has become increasingly difficult to give students detailed feedback. I don’t think this solves the problem completely, but it does give students distinct categories to work on in their writing.

SAMR Level & Future: This is mainly just a substitution, as rubrics have been used in teaching forever, and the rubric I use is just a digital version of the paper version with no interactive elements. In the future, as I continue learning Canvas's features, I would like to add a peer-review component. I think this might help to a certain extent with the deep feedback I cannot give them in larger and larger classes. I also think that engaging in peer feedback helps you begin to identify good and bad writing, if not always the why or how to fix the bad writing.

# data[].attributes.body
I recently learned through conversations with grad students about Notebook LM as a powerful tool for deepening student engagement with course readings and improving how they interact with complex texts. By allowing students to upload readings and then ask questions, generate summaries, or explore themes through an AI‑supported “conversation,” Notebook LM helps students manage large amounts of readings and interact with them in creative ways. I think this can be especially valuable for learners who struggle with dense academic language or who need support identifying key ideas before class discussions.

Within the SAMR model, use of Notebook LM seems to fit best under Augmentation. Students are still completing a familiar task, reading and preparing for class, but the tool enhances the process by offering clarification, personalized choices of how to interact with the readings, and opportunities to check understanding. It seems to improve accessibility and comprehension without inherently changing the nature of the need to engage with concepts.

Looking ahead, I can imagine using Notebook LM to move toward Modification, such as having students collaboratively build shared interpretations generated by the tool with their own or use AI‑supported insights to prepare richer in‑class discussions. Notebook LM may help create a more interactive, reflective, and inclusive reading experience that supports deeper learning.

# data[].attributes.headline
Gen AI integration into a writing assignment

# data[].attributes.subject
Gen AI integration into a writing assignment

# data[].attributes.body
I created a new assignment this January in a course that had a paper assignment. I separated out the parts of the assignment and had students use a Gen AI tool at differing levels to augment the assignment. It was effective learning for myself, my GTA and the students. It did likely have a big environmental impact from increased water usage to cool the data processing computers though. I am considering giving students the option to use it or not in future iterations of the course.

# data[].attributes.headline
EdX platform to host pre-readings, pre-reading quizzes, and homework

# data[].attributes.subject
EdX platform to host pre-readings, pre-reading quizzes, and homework

# data[].attributes.body
In couple of my courses, we are using EdX platform to facilitate pre-readings, pre-reading quizzes, and homework. In the past, pre-reading quizzes were offered as Canvas quizzes for students to complete prior to coming to the lectures. Students were expected to complete the assigned pre-reading materials from the textbook prior to completing the Canvas quiz. However, in EdX, we directly embedded the pre-reading materials within EdX as well as the quiz questions so that the students can complete the questions as they go through the reading. Within EdX, some interactive videos were also included to complement students’ reading. The homework questions were also located in the EdX platform.

EdX can grade students’ work automatically as well as it can provide students with multiple attempts to try a question. Additionally, there is an option to provide a grace period for students to complete and submit both pre-reading quizzes and homework.

Through the “grace period option”, EdX platform provides students with the autonomy to complete and submit work as they desire. I have used this feature in my courses and having this feature has influenced how I view about students’ learning, particularly the importance of students taking the ownership of their own learning process.

Adapting EdX platform to the courses belongs to the “augmentation” level in the SAMR model. This is because the nature of the tasks was the same prior to adapting EdX but having EdX improved the functionality of these tasks; quizzes embedded within reading along with tailored interactive videos to complement reading, and option to include a grace period to complete work.

# data[].attributes.body
Purpose: To organize, deliver, and manage digital teaching materials, assignments, and communication between teacher and students.

Impact: Google Classroom has shifted my planning toward more digital, interactive learning experiences. Instead of solely relying on printed worksheets or in-class instructions, I now build lessons that include videos, links, embedded documents, and collaborative tasks. This allows students to explore content more independently and revisit materials at their own pace. The platform supports ongoing communication beyond class time. Students can comment on assignments, ask clarifying questions, and collaborate on shared documents. I’ve noticed that students who are quieter in person feel more comfortable participating digitally, which leads to a more inclusive learning environment.

SAMR Model: At first, Google Classroom functioned as a Substitution/Augmentation tool, simply replacing paper handouts and adding conveniences such as easy submission and quicker feedback. Over time, my use shifted toward Modification, as assignments became redesigned to incorporate collaboration and digital feedback cycles that wouldn’t be possible with traditional methods.

Future Use: Designing project-based tasks where groups create digital portfolios or interactive presentations that connect with real audiences (ie. community partners). Integrating more cross-platform tools such as Jamboard, Flip, or generative AI for brainstorming and revision support. Using analytics from the LMS to personalize learning pathways and offer targeted extensions or interventions.

# data[].attributes.body
As simple as it might sound, learning more about Zoom in depth has helped me increase student participation during online small group sessions. Also, learning LLM has helped me generate more and diverse reflective questions to include during group discussions.

I believe it's both augmentation and modification. The first pertains to the uses and applications of Zoom, while the second involves using LLM to modify the way of creating reflective questions.

There is so much more that I am continuously learning and so much more to learn; it's fascinating!

# data[].attributes.body
In the last course I taught I decided to create my own animations to represent concepts in a more obvious digital way (as opposed to just having pictures in my slides). Initially it started as something that I wanted to do for fun, but it vastly improved student engagement and allowed for some injections of humor which seemed to increase the rate that students remembered and internalized the concepts.

I believe this would fit into the Augmentation part of the SAMR model, as the animations would act as an augmentation to existing digital figures in the material.

For future use, I would be interested in using some more generative AI to help create even more complex animations and figures (assuming their accuracy could be maintained).

# data[].attributes.body
Technology:
I enjoy using Slido with word clouds to encourage student participation in an anonymous, low-stakes manner.

Impact:

Using Slido has changed my lesson design as it requires some level of flexibility in allowing time for students to generate responses and to discuss the most salient ideas that populate the word cloud.  Student engagement is generally much better than if I as a question and ask for individual oral responses.  It also helps with assessment, allowing me to quickly gage the general understanding of the class by analyzing the most popular responses.

SAMR:

To me this is a modification - I’m going beyond having students submit individual responses, that only I see, and clouding their responses together so that they can all see each others’ responses to gage each others learning and provide feedback/comments in discussion with me.

Future Use:

I could use this more frequently in the future to ask the same question at multiple points throughout the class’ learning to see how the word cloud changes throughout the course as they learn and integrate new concepts.  I could show them the change in the word cloud over time at the end of the course to demonstrate their learning progress to them.

# data[].attributes.body
I recently had a meeting with a publisher and found out that they provide free consultation, material and Canvas support when creating a course. They have a representative at UBC V or O that helps us build the course with all features: textbook figures and slides, assignments, exams, etc.

This changes the way of planning the lesson design and assessment methods, while saving my time.

SAMR Model: it substitutes the traditional lecture creation from scratch in PowerPoint, as well as augments assessment possibilities.

# data[].attributes.body
Purpose: to increase student engagemennt and build community: I used Slido in a few ways - first to give a "fun" poll at the beginning of class (i.e., what did you do over reading week?) This got students on the platform (no app or downloads or sign ins required). Then, I'd do an anonymous review quiz that talked about last class's content to test their understanding. If performance was poor, I'd review concepts. I left slido on and throughout class, students could anonymously ask me questions. This helped with accessability as some students were nervous to speak up in class.

Impact on Teaching: my students LOVED slido. It helped them learn, gave them a voice, and also allowed me to connect with them. They looked forward to my daily polls.

SAMR Level & Future use: to me, this is in the "R" category - I previously could not anonymously ask questions or poll students in this way. I plan to continue to use this tool :)

BTW - slido is free for courses up to 90 students.

# data[].attributes.headline
Microsoft Copilot - for refining student research questions (Sample Post)

# data[].attributes.subject
Microsoft Copilot - for refining student research questions (Sample Post)

# data[].attributes.body
Technology & Purpose: Microsoft Copilot – helps students refine research questions and create outlines.

Impact on Teaching: Saves time on topic formulation, letting us focus more on analysis and critical thinking. Students learn to assess and adapt AI suggestions.

SAMR Level & Future Use: Modification – plan to use it for students to co-create interactive study materials for peer review.