BC TEAL 2026 Annual Conference

E-learning design is not just about technology. It is about the intersection of technology, content and pedagogy and based on deep practical insight from the classroom. Here we will share experiences with designing for lower Stage 1 learners. 

In this presentation we will outline key reasons why CLB 1 & 2 learners need digital skills for life in Canada. We will also discuss e-learning design considerations for low Stage 1 LINC learners including device use, literacy issues and tech familiarity and lead a discussion in the workshop. 

We will also showcase 1. H5P activities, including multimedia choice, 2. Wordwall, and 3. Simple YouTube videos and accompanying worksheets. We will also demonstrate how CLB 1 and 2 e-activities can be created from authentic materials and simulate real-world experiences and discuss the ways e-activities can be used in different modes (F2F, Zoom, asynchronously). Resource lists and cheat sheets on how to make the activities will be shared. 

As English as an Additional Language (EAL) programs adopt online platforms, assessments often trail in design quality. Simply porting paper tests to screens erodes reliability, validity, and equity. This interactive session introduces a digital mindset—a flexible, learner‑centred, problem‑solving stance that emphasizes intentional tool choice, data‑informed iteration, and practical constraints. Grounded in Social Constructivism, we frame assessment improvement as a socially mediated process in which knowledge is co‑constructed through dialogue, collaboration, and shared reflection. Participants will engage in spectrum positioning, scenario analysis, and group redesign tasks that model constructivist practices and foster collective sense‑making.
We begin with the “Digital Mindset Spectrum” to activate prior knowledge and surface diverse comfort levels. Short, realistic scenarios then highlight common pitfalls of adopting tools for speed rather than pedagogy, moving tests online without rethinking timing or formats, and misaligning external scores with local outcomes. Drawing on The Digital Mindset to clarify what it takes to thrive amid data, algorithms, and AI (Leonardi & Neeley, 2022), and Co‑Intelligence to position AI as a collaborative partner in assessment workflows (Mollick, 2024), we explore practical ways to enhance design while protecting integrity.
A concise framework organizes five essentials for digital placement assessment: reliability, validity, security, accessibility, and practicality. Using common Canadian university pain points, participants diagnose challenges in writing, listening, speaking, and reading placements, then propose possible improvements. Attendees will redesign a placement task for digital delivery to clarify instructions, reduce construct‑irrelevant difficulty, plan for accessibility, and add features such as adaptive logic, automated feedback, and test security.
We conclude with a five‑question decision tool that centres pedagogy, equity, actionable data, instructor workload, and program outcomes.
By the end, participants will:

• Define and apply a digital mindset in assessment design.
• Leverage AI as a co‑intelligent aid while maintaining standards.
• Implement one concrete, constructivist‑informed change to strengthen their assessments.

References
Leonardi, P. M., & Neeley, T. (2022). 

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.

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.