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College of Education

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    Preparing for the demands of tomorrow: Using AI to support grade 12 academic writing instruction
    (2024) Korchinski, Katrina
    Preparing secondary students for the rigor of post-secondary academic writing goes beyond teaching the five-paragraph essay and grammatical conventions. The arrival of generative artificial intelligence (GAI) requires educators to understand the intricate relationship between technology and literacy. Building AI literacies in student writers is essential, as GAI is here to stay. Recognizing its potential, adapting teaching methods, and integrating AI literacy into writing instruction is crucial for preparing the next generation of communicators for their academic and professional futures. Those who value writing refute the notion that students no longer need to learn how to write, and current literature shows that GAI does not eliminate the need for skilled writers but rather signals a shift in literacy instruction. While GAI may offer unethical shortcuts, students must still learn how to independently express their ideas, demonstrate knowledge, and engage in critical thinking. They need to learn to use GAI tools as aids, not crutches. To enter university as confident writers, secondary students require scaffolded instruction, mentorship, practice, review, feedback, and reflection, all supported by the ethical use of GAI tools. This project focuses on teaching ethical use of AI in writing and enhancing composition through the Cognitive Process Theory of Writing. Lesson plans and resources are included to encourage teacher integration of AI literacy into classroom writing instruction.
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    Preparing Educators to Teach and Create With Generative Artificial Intelligence
    (Canadian Network for Innovation in Education (CNIE), 2024) MacDowell, Paula; Moskalyk, Kristin; Korchinski, Katrina; Morrison, Dirk
    Teachers skilled in using generative artificial intelligence (GAI) have advantages in terms of increased productivity and augmented instructional capabilities. Alongside the rapid advancement of GAI, teachers require authentic learning opportunities to build the confidence and expertise necessary for engaging with these technologies creatively and responsibly. This article provides an illustrative case of preparing preservice and in-service teachers with the knowledge, skills, and mindsets to teach and create with GAI. Using a self-study method to investigate professional practices, we analyzed the curriculum, instruction, and assessment in an upper-level undergraduate course in multimedia design and production. Thirty-five teachers engaged in experiential activities focussed on developing artificial intelligence (AI) literacy, alongside a collaborative assignment to co-author an open-access textbook, Teaching and Creating With Generative Artificial Intelligence. To support equitable and inclusive access to the educational benefits offered by AI, the Student Artificial Intelligence Literacy (SAIL) framework was developed. SAIL facilitates student AI literacy through curriculum engagement and three distinct types of interactions: cognitive, socio-emotional, and instructor-guided. Building on lessons learned from the COVID-19 pandemic regarding the issues with technology training for teachers in Canada, five recommendations are offered to facilitate the meaningful integration of AI literacy in teacher education programs.
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    Reparative futures
    (Futures, 2024-09-14) Myers, Kevin; Nally, David; Paulson, Julia
    The past is present in all future making activities. However, there is more that futuring processes can do to engage with past-present relationships, namely by bringing to the fore frameworks of reparation and redress. This article explores how ideas of reparative action may offer generative resources for Futures Studies. It suggests that in order to create futures characterised by justice it is essential to listen to and engage with ongoing histories of repression, violence and domination and find ways to talk about the past that support individuals, communities and nations to reimagine and remake social relations that are just and inclusive. The article explores reparative futures as they are negotiated in practice, through the lens of their pedagogical potential and ethical demands, and as world-making political possibilities. In doing so, it highlights the necessity for enhanced dialogue between Future Studies and the ‘reparative turn’ within the humanities and social sciences. We explore the tensions and unresolved questions of reparative futures along with the possibilities for future-making practices characterised by justice, care, creativity and humility for humans and nonhumans.
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    Child Labor in Sindh, Pakistan: Patterns and Areas in Need of Intervention
    (Stats, 2024-11) Maqbool, Nadia; Newton, Paul; Shah, Tayyab
    Child labor remains a predominant issue in Pakistan despite the country’s existing policies and frameworks aimed at abolishing it. Through this study, we investigated the child labor distribution across Sindh and examined the factors that shape the regional patterns. We analyzed the data available through the 2018–19 Sindh Multiple Indicator Cluster Surveys, MICS 6, from 20,030 households with 40,633 children in the 5–17 age bracket. By applying prevalence statistics, chi-square tests, and regression modeling to these data, we investigated the trends in child labor prevalence, identified the correlation between child labor and various socioeconomic and geodemographic variables, and finally mapped the geospatial patterns of child labor across districts in Sindh, enabling us to identify and prioritize the districts in need of immediate intervention. The findings revealed that about 20 percent of the children in Sindh are engaged in child labor, with a high prevalence among males and in the 15–17 age bracket. Moreover, poverty and rural dwellings raise this issue. Other socioeconomic and geographic factors reinforcing this issue are a lack of education among children, mothers, or caretakers and mothers’ or caretakers’ functional difficulties. However, children’s functional difficulties lower their prevalence in labor. Among the 29 districts across Sindh, Kambar Shahdadkot has the highest prevalence of child labor.