BC TEAL 2026 Annual Conference

GenAI can help EAL instructors tap into scalability to fill in the gaps when providing individualized feedback, saving hours of typing out every suggestion. Drawing on personal EAP classroom this academic year, the research focus is practical, using GenAI to enhance instructor-guided feedback on writing and speaking tasks to include personalized examples and practice for each learner. The presenter will share tailored prompts focusing on common learner weaknesses in writing structure (e.g., clarity, organization, and word choice) and on suprasegmentals in pronunciation (stress, rhythm, intonation), without overwhelming students or instructors. The goal of the session is to share what worked and what didn’t. Successes included prompts that stayed focused on one skill at a time, feedback delivered in digestible formats, and practice activities that could raise student’s awareness and help to self-identify weaknesses for future tasks. Challenges included trying to do too much for each stage of the assignment and producing feedback that felt too long. The session will link each challenge to a practical adjustment that attendees can adopt immediately. Survey results reporting student reactions and preferences will also be discussed when receiving AI feedback, including hesitation to trust AI feedback and the importance of Instructor guidance. By the end of the session, attendees will leave with a set of prompt templates to use for scalable, individualized feedback prompts while, hopefully, avoiding some of the pitfalls. 

This session will explore how teachers can use AI to produce formative, annotated feedback on student writing without losing the personal touch.


We’ll look at examples where large language models generate margin comments that highlight strengths, note recurring issues, and suggest next steps - while keeping teacher voice, tone, and judgment central. Participants will learn practical prompting techniques, ways to scaffold AI-generated feedback for different proficiency levels, and ethical considerations such as transparency and student data privacy. 


We will use Google AI Studio to create a simple workflow that allows teachers to:


- Paste in text from a student writing assignment
- Generate color-coded and categorized annotations directly on the text
- Import assessment criteria or rubrics to guide AI-generated feedback


The focus is on prompt design, feedback categorization, and practical classroom application. Teachers will leave with a working prototype they can adapt independently.

As artificial intelligence becomes more visible in education and assessment, many educators and administrators have questions about how AI is actually used in language testing, and how concerns about fairness, bias, and validity of AI-enhanced language tests are addressed in real-world settings. This session provides a practical, non-technical overview of how AI supports test design, administration, and scoring in a high-stakes English proficiency assessment.
Using the Duolingo English Test as a concrete case, we describe how AI tools assist with tasks such as generating test content, managing secure test delivery at scale, and supporting scoring processes. Rather than replacing human expertise, these systems are intentionally designed to operate within a human-in-the-loop model, where assessment specialists review AI-generated materials, monitor test performance, and intervene when anomalies or equity concerns arise.
The session emphasizes process over theory, focusing on how responsible AI use is operationalized in practice. Topics include how test developers determine which components are appropriate for automation, how quality control and bias checks are embedded throughout the test lifecycle, and how accountability is maintained when automated systems are involved. The role of transparency—both for institutions using test scores and for test takers themselves—is also addressed.
In addition, the session considers issues of access and inclusion, illustrating how technology can expand global testing opportunities while maintaining consistent standards. Participants will leave with a clearer understanding of how AI is currently used in cutting-edge language assessment, what safeguards are in place to protect test takers, and how educators and administrators can critically evaluate AI-assisted tests when making admissions, placement, or policy decisions.

Artificial Intelligence (AI) is increasingly shaping how learners access information, communicate, and learn languages. From translation tools and grammar checkers to AI-generated images, videos, and voices, learners are interacting with AI daily—often without the skills needed to understand how these tools work or how to use them safely, critically, and effectively.
This session introduces a new AI literacy course  in Avenue.ca for adult ESL and settlement-focused language programs at CLB 1-4.  The activities are  aligned with the Avenue Learner Standards for Technology-Enhanced Language Learning (TELL). The course supports learners in building a foundational understanding of AI and generative AI, recognizing AI-generated and manipulated content, and developing awareness of how AI can both support learning and be used to mislead or cause harm.
The course is highly visual and experiential. Learners engage with a wide range of AI-generated images, videos, and audio samples to observe how realistic AI content can appear. Hands-on activities guide learners through identifying common features of AI-generated media, checking sources, and questioning accuracy. Several AI engines and AI-powered language learning tools—such as translation, pronunciation, and writing support apps—are introduced, with step-by-step instructions that help learners use these tools effectively while understanding their limitations.
The presentation will showcase how the course integrates interactive tasks, guided discussions, and reflection activities that promote learner agency and critical thinking. Participants will also see how the course explicitly addresses Avenue standards related to digital safety, ethical technology use, and independent learning.
Attendees will leave with practical insights into designing AI-aware lessons, concrete examples of learner-friendly activities, and strategies for supporting learners as they navigate AI tools and information in their everyday lives and communities.

This session adult language educators will be introduced to a GenAI tool and the Government of Canada's advice on careful adoption, usage or rejection of new GenAI technologies.  Participants will walk participants through an example of story book generation Google Gemini’s storybook feature. Although the results are initially impressive, educators need to look more closely when using GenAI tools. When we are considering GenAI offerings, we should not just focusing on tool mastery. In this session, we will examine a generated story to apply the Government of Canada's FASTER principles (Fair, Accountable, Secure, Transparent, Educated, Relevant). Instructors examine cultural representation, data privacy, transparency with learners, pedagogical value and instructional fit. The session models how teachers can critically assess AI generated materials while maintaining professional judgment and learner-centered decision-making. Participants leave with a practical framework for evaluating  GenAI tools. 

English as an additional language (EAL) students have increasingly been found to use AI-powered technologies in their learning process (Du & Yang, 2025). However, existing research has largely focused on the affordances and challenges of these tools, paying limited attention to how students’ English language proficiency shapes their engagement with AI-mediated writing. Based on a case study of six EAL graduate students at a Canadian university, this research addresses this need by posing the question: To what extent does English language proficiency enable EAL graduate students to critically engage with AI-generated writing suggestions and resist the standardized language ideologies embedded in these tools?
Data were collected through observations of AI use in writing, semi-structured interviews, and digital artifacts such as screenshots of writing processes and generated texts. Thematic analysis followed an inductive approach, complemented by multimodal analysis in which artifacts were annotated and linked to emergent themes.
Positioned within critical language awareness (Fairclough, 1992; Motha, 2014), findings reveal that participants’ levels of English proficiency significantly shaped engagement with AI technologies. As these graduate students have advanced English language proficiency, they strategically used AI as a reference for their writing process, questioning suggestions, refining prompts, challenging standard language ideologies, and selectively incorporating revisions to preserve their authorial voice. The study also found that AI tools promoted standardized academic conventions, influencing lexical choices and syntactic complexity. Students with stronger linguistic confidence were more likely to resist homogenizing effects and maintain ownership of their texts. 
The presentation concludes by arguing that English language proficiency should be understood as a foundation for critical language awareness in AI-mediated contexts. EAL classrooms should position English language proficiency not merely as accuracy or fluency, but as a resource for critical engagement with AI, equipping learners to recognize, negotiate and resist the standardized language ideologies these tools promote.

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.