^{Themes: Assessment, Ethical challenges in using AI, How I’ve been using AI, Specific AI Tool(s), Teaching Strategies
Audience & Subject: Grades 9-12; General, Literacy}

Introduction

As many new teachers start their careers alongside unprecedented generative text AI advancements, it is imperative that the pedagogical supports accurately address concerns that may arise. From Chatbots to Triangulation aims to provide new teachers and all those mentoring and working with them with tangible steps based on those early career experiences and departmental guidance. Below, I share preventive measures for limiting AI use, an AI-spotting “cheat sheet,” and other experiences to help you on your journey.

Setting Yourself Up for Success: Preventative Measures

The following steps can function as preventative measures for AI use in their classrooms:

1. The collection of “low-stakes” written work or evidence of learning (e.g. diagnostics, get-to-know-you forms, quizzes, emails, etc.).
2. Relationship-building to understand who students are and their authentic academic, social, and emotional needs, thus aiding in conversations addressing academic dishonesty.
3. Casting a wide net of support by connecting with all relevant school aids (e.g. student success, special education, guidance, ESL/MLL department) to ensure equitable assessment and implementation of accommodations ahead of reliance on AI.

The AI-Spotting “Cheat-Sheet”

After considering preventative measures, ask the following questions to investigate student work:

1. Does the work sound like the students’?
2. Did the work appear in an unrealistically fast amount of time?
3. Is there limited process work attached to the task?
4. Is the student struggling to explain their thinking?

Harnessing the Power of Triangulation

  Initially, consider the statement that “teachers use a variety of assessment strategies to elicit information about student learning [that] should be triangulated to include observation, student-teacher conversations, and student products” (Growing Success, 2010, p. 39). Next, teachers can use the insight to guide course planning to ensure equitable assessment and limit AI use by focusing on observable skills. Something that students can be seen doing, can explain their thinking around, and produce a final product on. The outcomes should highlight student skills while reflecting rising concerns around academic dishonesty.

Building Expectations/Skills-Based Rubrics

  A starting point for building expectations/skills-based courses includes:

1. Deconstruct the curriculum by isolating the “skills” located in the overall and specific expectations (the verbs).
2. Ask yourself, “How can these skills be observed?”
3. Ask yourself, “Can these skills be explained and/or talked about? Can students explain their thinking?”
4. Ask yourself, “Which products would best demonstrate this learning at the end of this process?”

Possible Challenges

It is essential to note the difference between subjects in their respective Ontario curricula. The actionable verbs differ greatly from subject to subject, as do teacher comfort levels, especially in courses with long-standing course shells where starting over may feel like a large obstacle. In this case, I recommend connecting with new teachers to help gather new insights that build upon existing courses and foster a group-based growth mindset to adapt to modern challenges. Furthermore, despite efforts to address AI use by taking preventative measures and focusing on observable skills, there may still be academic dishonesty cases, especially as more AI tools become available.

Support Materials and Resources

Should academic dishonesty continue to prove a concern for teachers, several helpful and low-barrier options include using:

1. Google Docs and reviewing its version history,
2. The Revision History Google extension, and
3. ZeroGPT is a free detector tool designed to detect AI-generated text.

I encourage you to embrace AI use when appropriate through board-approved tools like Perplexity and Microsoft Co-Pilot. Using AI to showcase skills within expectation/skills-based courses could also prove effective. Good luck in your journey with AI!

References

Ontario Ministry of Education. (2010). Assessment for learning and as learning. In, Growing Success: Assessment, Evaluation, and Reporting in Ontario Schools (pp. 38-46). https://www.edu.gov.on.ca/eng/policyfunding/growsuccess.pdf

About the author

name: Marjan Mahmoudian

Marjan Mahmoudian (HBA, B.Ed, OCT) is a recent graduate from Ontario Tech University and a first-year English teacher in the York Region District School Board. Though her interests typically focus on the intersection between place-based, experiential, and anti-racist education, her first year of teaching has been centred around the rise of AI in the English classroom. With guidance and support from her department, Marjan has developed her abilities in using triangulation and expectations-based rubrics to address academic dishonesty and ensure equitable assessment. Marjan has recently shared her insights as a new teacher with English Department Heads. She hopes that sharing these experiences will inform new teachers and all those guiding them on how best to approach their practice in this ever-evolving pedagogical landscape.