BC TEAL 2026 Annual Conference

Language Instruction for Newcomers to Canada (LINC), as well as most provincially funded programs, are rooted in the CLB. As AI tools become more accessible and relevant, they are increasingly shaping teaching and learning practices in these programs, particularly at a time of sector restructuring. Educators are therefore faced with urgent questions of alignment and pedagogical appropriateness of these tools. This convergence raises critical questions about how AI use interacts with the CLB and what we are learning from early adoption of this technology.      
 
This fireside chat brings together knowledge experts and practitioners in CLB, second language acquisition, and generative AI to discuss AI applications for the CLB, including benefits and limitations. Participants will learn about the different ways AI is currently being used with the CLB as well as about potential future applications. 
 
The objectives of the sessions are to:
(1) examine how AI is being used in CLB-based instructional and assessment contexts;
(2) critically explore benefits, risks, and limitations of AI for language instruction and assessment;
(3) support educators in making informed, pedagogically sound choices about AI integration rooted in the CLB.
 
Rather than individual presentations, the moderator will engage the 3-person panel in a 35-minute focused discussion, eliciting responses, reactions, and thoughtful interactions in a natural, yet structured exchange. There will be 10 minutes for questions from the audience at the end. 
 
Among the topics discussed will be principled uses of AI in CLB-aligned content generation, such as creating realia for real-word tasks, classroom materials, and lesson plans; how it can be used for automated scoring and qualitative feedback in evaluation for different skills; how AI tools can ease teacher workload and help them navigate resources and documents; and how AI can develop both teacher and learner’s skills in using AI. 

Given the increasing attention to and application of Artificial Intelligence (AI) in education, investigating its ethical use is timely. AI safety concerns are not limited to deep fakes. They also include the ethical application of AI in classrooms such as bias, fairness, transparency, and academic integrity. The studies which have investigated using AI in education tend to lack ethical recommendations for educators. To address this gap, this study, informed by the Seoul Declaration for safe, innovative, and inclusive AI, will aim to review the application of AI in education and offer recommendations for educators to ethically employ it. First, the historical background of AI and the current landscape of AI in K-12 and higher education in international contexts will be explained. Then, the application of AI in Education will be reviewed, and ethical concerns will be examined. Finally, best practices for K-12 and post-secondary educators to implement AI in class will be offered.

In their use of generative artificial intelligence (GenAI), EAL educators can both reinforce and challenge dominant language ideologies, reflected in how large language models tend to privilege standardized practices in English, Indigenous, and other named languages (Stap & Araabi, 2023; Lau, 2024). This presentation explores how developing and enacting a translanguaging stance—a belief system for leveraging learners’ full linguistic repertoires holistically (García et al., 2017)—can support the critical use of GenAI in EAL instruction, especially when the educator and their students do not share a similar linguistic and cultural background.
Drawing on his Design-Based Research (DBR) project, the presenter demonstrates how ChatGPT was used in translanguaging-informed ways to make instructional materials more linguistically and culturally responsive for teaching Japanese exchange students in a sociolinguistics course. This work involved the deliberate integration of: (1) translanguaging theory grounded in antiracist, decolonial, and poststructuralist perspectives (Li, 2018); (2) emerging knowledge of Japanese language and sociocultural norms; (3) complementary digital tools such as DeepL to support proofreading and cross-checking translingual outputs where appropriate; and (4) the linguistic and cultural expertise of students and colleagues, including a teaching assistant proficient in Japanese.
Through short practice-based vignettes, the presentation illustrates how such critical GenAI use strategies can potentially help identify, negotiate, and disrupt deficit-oriented language ideologies embedded in GenAI-generated output. The findings highlight both the pedagogical possibilities and ideological tensions of GenAI use, and offer practical implications for EAL educators seeking to design or revise teaching materials through a translanguaging-informed, socially just lens in Canada and beyond.

We've all heard countless talks about using AI to create academic material. This session instead explores practical ways language educators can adapt existing paper-based materials using AI tools. By digitizing and adapting familiar worksheets, teachers can create level-appropriate, interactive versions that better meet students’ diverse needs without starting from scratch.


We’ll walk through examples of how large language models can simplify, extend, or differentiate worksheet content, and discuss what this means for curriculum design, accessibility, and teacher autonomy. 


In this session, I will demonstrate how teachers can use AI to:

• Scan or upload an existing worksheet
• Deconstruct the worksheet into its functional components
• Render the content into an editable format
• Revise and differentiate the worksheet through a combination of prompting and manual editing

The emphasis is on adapting existing materials rather than generating new ones from scratch, with workflows that teachers can implement independently.

Generative AI (GenAI) tools such as Microsoft Copilot and ChatGPT are becoming increasingly capable of offering detailed, pedagogically useful feedback on student writing, particularly in areas such as grammar, diction, clarity, organization, and academic style (Alnemrat et al., 2025; Gjorevski et al., 2025; Hyland, 2025; Ofgan, 2024; Yin, 2025). This Ed Tech Jam session demonstrates how Copilot can serve as a practical support tool for teachers working with EAL learners in EAP contexts. 
During the session, the presenters will use a sample student essay and showcase Copilot’s capabilities by prompting the AI tool to: 1) Distinguish whether the student text appears human‑ or AI‑generated. 2) Apply two rubrics, one teacher‑provided and one AI‑generated, to assess the sample student essay and justify the scoring. 3) Provide targeted feedback on grammar, diction, and clarity. 4) Comment on content development and organizational effectiveness. 5) Check APA citations and reference formatting. 
The presenters will also discuss pedagogical strategies for integrating Copilot into feedback workflows, including using AI for formative feedback, preliminary assessment, or initial comments that are reviewed and refined by the teacher in a complementary hybrid fashion.
Participants will have hands‑on opportunities to try Copilot for the five tasks above and explore additional prompts of their choosing. They will also share questions and insights related to AI feedback reliability and responsible classroom integration.
This hybrid human‑AI approach especially supports large‑class essay marking, accelerates formative feedback, and assists less‑experienced teachers. Participants will leave with a set of adaptable prompts and a clearer understanding of Copilot’s strengths, limitations, and potential for enhancing feedback practices in EAP writing classes. 
 
 
 
 
 
 

As generative AI (GenAI) tools become commonplace in higher education, many instructional responses continue to focus on restriction, detection, or punitive approaches to academic integrity. This practice-based presentation offers an alternative: a postplagiarism-informed teaching strategy that makes GenAI use visible, intentional, and ethically grounded within a creative problem-solving task. Postplagiarism emphasizes that while learners may outsource aspects of their work to GenAI tools, human responsibility and accountability for the final output remain central (Eaton, 2023). Drawing on classroom practice in undergraduate management education, this session introduces a short, structured activity in which students are explicitly encouraged to use GenAI tools while documenting and reflecting on both human and GenAI contributions across stages of creative problem solving, including problem framing, idea generation, solution development, and evaluation. By designing the activity to surface rather than conceal GenAI use, students engage in ethical decision-making, develop judgment about when and how GenAI is appropriate, and take ownership of their learning. For instructors, this approach supports academic integrity without reliance on surveillance technologies, while fostering trust, transparency, and student engagement. We will briefly outline the postplagiarism framework, describe the classroom activity, and share key instructional insights gained from its implementation. Although situated in management education, the approach is adaptable across disciplines and learning contexts.