New Kinds of Educational Tasks and Projects
The challenge here is to create new kinds of educational tasks and projects that foster meaningful collaboration between students and AI, while ensuring that students develop essential skills and avoid relying solely on AI for their work.
Before diving into the five new kinds of educational tasks and projects, it is important to note that these are suggestions and starting points for experimentation in the classroom with students. Educators should feel free to adapt and modify these approaches to best suit their specific teaching context and the needs of their students. The examples provided are geared towards students in the creative field, but these ideas can be applied across various disciplines.
New Kinds of Educational Tasks and Projects
AI-supported inquiry-based learning: Students can collaborate with AI to develop research questions and hypotheses related to real-world issues. The AI can help identify relevant resources, but students must analyze and synthesize information to draw conclusions. This approach emphasizes critical thinking, information literacy, and ethical decision-making. To address assessment challenges, scaffold the process with milestones, such as research question submission, literature review, and final conclusions. Example: Students in a graphic design course might explore the impact of cultural appropriation in design. They work with AI to identify examples and analyze the ethical implications of using elements from different cultures in their work.
AI-enhanced peer teaching: Students work in pairs or small groups, with one student serving as the "teacher" and using AI as a resource to prepare and present a lesson on a specific topic. This will foster collaboration, communication, and cross-cultural competency. Example: In a photography class, a student might use AI to research and present the history and techniques of street photography, while their peers provide feedback and engage in a discussion about the topic.
AI-guided project-based learning: Assign students interdisciplinary projects, using AI tools to support the research, design, and implementation process. Require students to address the ethical implications and societal impacts of their projects. Develop adaptability, resilience, and problem-solving skills. Example: Students in an advertising course might be tasked with creating a socially responsible ad campaign, using AI to research target audiences and brainstorm ideas while considering the ethical implications of their work.
AI-assisted multimedia creation: Students create multimedia presentations (e.g., videos, podcasts, interactive websites) on a given topic, using AI to generate initial ideas or gather information. Students must refine, organize, and present the content in a coherent and engaging manner. Example: In a visual storytelling class, students could use AI to help generate ideas for a short animated film, then create the storyboard, script, and final animation, showcasing their creativity and storytelling skills.
AI-mediated cross-cultural collaboration: Connect students from different cultural backgrounds to work on collaborative projects using AI as an intermediary and support. Encourage students to explore cultural differences, similarities, and perspectives, fostering cross-cultural competency and empathy. Example: Students from different countries collaborate on designing a virtual art exhibition, using AI to research art styles, history, and artists from their respective cultures. The final exhibition would highlight both the unique and shared aspects of their artistic traditions.
To ensure meaningful human-AI collaboration and active student engagement, the grading criteria for these tasks should emphasize critical thinking, creativity, and effective collaboration with AI. Incorporate checkpoints, progress reports, peer reviews, and self-assessments that require students to reflect on their learning process and the role AI played in their work. These assessments, along with instructor evaluations, will help mitigate vulnerabilities and prevent overreliance on AI. Additionally, using plagiarism detection tools can deter copying AI-generated content without proper evaluation and modification, further promoting genuine collaboration and skill development.
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