Taking charge: Student and educator use of AI

ISTELive 24 in Denver was an active, engaging experience for more than 16,500 attendees and provided evidence that ISTE continues to inspire educators interested in technology. Artificial Intelligence (AI) was a strong theme throughout the conference, from keynotes to research sessions to hands-on activities. We suspect that almost every attendee left the conference with a better understanding of both opportunities and challenges for AI use in education. One general area of agreement was that AI opportunities in education will continue to accelerate in the next few years and that educators need to work to become active participants in AI research and development.

AI creates a new challenge for teacher educators working to determine and model effective uses of AI for both preservice and in-service teachers. A major theme in the work is to emphasize the need for educators to take charge of their use of AI with a careful understanding of the clear limitations of AI use. In most AI sessions we attended, educators were encouraged to spend time “playing” with AI to understand its capabilities and possibilities better.

We are already seeing examples of teachers introducing AI activities in classrooms that empower learners to use AI in useful and appropriate ways. These activities make it clear that the learner is in charge of using AI and needs to view AI as a tool and thought partner. Teachers worldwide are becoming “experts” in AI and working to empower their students as critical, knowledgeable, and ethical AI users. In fact, we witnessed this approach while visiting a class of second graders in Iowa this past spring.

The AI learning activity we observed was designed to introduce second graders to the concepts of machine learning. Carrie Hillman, the school’s technology integration specialist, taught and facilitated this series of activities to build students’ understanding of how AI works. Initially, the students participated in several preliminary exercises to ensure the students understood some fundamental principles of machine learning. First, the students engaged with Google Quick Draw (Quick Draw!, n.d.). This activity allowed these young learners to observe the data and discern patterns in the images that the machine had learned to represent. Following this, the students participated in the Code.org AI for Oceans activity (AI for Oceans #CSforGood, n.d.), providing them with firsthand experience in training a machine to recognize different objects. For another activity to support the concepts of machine learning, the students input data sets into the Teachable Machine which also reinforces their understanding of the patterns being analyzed by the machine (Teachable Machine, n.d.).

Building on their foundational knowledge of machine learning, the second graders then applied their understanding of machine learning to generative AI by creating their own AI images. Utilizing a Canva slide deck template titled “Real or AI,” students selected or generated images using Canva’s built-in AI tool (Canva, n.d.). The objective was to determine if observers could distinguish between real and AI-generated images.

Upon completing their slide decks, the students created Google Sheets to collect data on whether people believed the images were real or AI-generated (Google Sheets, n.d.). Students gathered data by asking others in the classroom or individuals in other areas of the school—like the hallway, lunchroom, and gym—whether their images were real or AI-generated. Students then analyzed the data to identify images that were too easily identified as real or AI-generated. Some students chose to regenerate images when their data indicated a 100% correct identification rate. This activity was highly engaging for the students, especially when they successfully deceived adults and students with their creations.

The success of this AI learning activity paves the way for a natural transition into lessons on fake news in the following school year. Students, at a young age, were learning to critically evaluate the content they encounter online and to recognize patterns in AI-generated images. These second graders quickly noted that AI often struggles with rendering hands and feet accurately, with many AI-generated images displaying blocky feet or extra fingers and toes! This learning activity illustrates how a foundational understanding of AI and machine learning can enhance students’ digital literacy and prepare them for more complex discussions on media literacy, critical thinking, and responsible digital use in the future.

We hope that sharing the context of this AI learning activity will inspire other teacher educators and classroom teachers to discover the creative capabilities of using AI with students. In this issue of JDLTE, a couple of the articles focus on the research being conducted around AI, especially with preservice teachers. The first article, Effectiveness of a Professional Development Program Based on the Instructional Design Framework for AI Literacy in Developing AI Literacy Skills Among Pre-service Teachers, highlights a study conducted to address preservice teachers’ AI literacy. The article describes a professional development program designed to develop preservice teachers’ AI literacy skills specifically. The authors’ findings support efforts for using AI tools within preservice and in-service training programs. The article titled, An Exploration of Preservice Teachers’ Perceptions of Generative AI: Applying the Technological Acceptance Model, examines elementary preservice teachers’ perceptions of using Generative AI (GenAI) while using it for a read-aloud activity while taking a literacy methods course. The study’s results are encouraging, and the authors provide a rich context for replicating the activity. The final article, Preservice Teachers’ Belief Toward Online Learning and Future Teaching: Redesigning Teacher Training, offers insights into preservice teachers’ beliefs about themselves as online learners and future goals for teaching online. This longitudinal study uses the Dynamic Change of Personality Traits theory to frame its investigation. Findings suggest that experiences preservice teachers have in online environments as learners may impact their beliefs toward future online teaching. Enjoy this JDTLE issue!

Ann D. Thompson and Denise A. Schmidt-Crawford
Iowa State University, Ames, IA, USA
[email protected]
Carrie M. Hillman
Nevada Community School District, Nevada, IA, USA
Denise L. Lindstrom

Disclosure statement

No potential conflict of interest was reported by the author(s).