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.