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Developing AI Literacy Across the Curriculum: A Guide for Programs and Faculty
The rapid integration of AI into professional practice across disciplines makes AI literacy increasingly crucial, not just for technology-focused fields but for all areas of study. Even faculty who are skeptical of AI's value need to consider how it's transforming their disciplines. For example, scientific fields are seeing AI adoption in literature reviews, experimental design, and data analysis. In the humanities, AI tools are already being used for textual analysis, translation, and content creation. Creative disciplines must grapple with AI's impact on artistic production and copyright. Professional programs face increasing pressure from employers who expect graduates to understand AI applications in their field.
Integrating AI Into Assessments: From Policy to Practice
Conventional assessments, such as essays and multiple-choice questions, have long been the cornerstone of evaluating student performance. However, the widespread availability of generative AI (genAI) tools necessitates rethinking assessment methods. Now that genAI tools are readily accessible and rapidly improving, it is crucial to develop assessment approaches that maintain academic integrity while leveraging the benefits of AI to engage students and prepare them for the modern workforce (Yu, 2023).
Fostering Deep Learning and Motivation in the AI Era
As generative artificial intelligence (genAI) reshapes the educational landscape, faculty must rethink traditional assessment strategies to maintain academic integrity and real-world relevance. This piece explores strategies for creating effective assessments in an AI-mediated world, focusing on two key areas: collaborative activities that develop essential human skills, and formative assessments that emphasize personal growth and deep learning. These approaches not only address concerns about AI misuse but also prepare students for future workplaces where human capabilities will complement AI tools.
Problem-Based Learning
Problem Based Learning is a teaching method used to facilitate student knowledge acquisition. This teaching method is often confused with Project Based Learning, which centers on students applying knowledge. The focus of Problem Based Learning is students acquiring the knowledge. Since the two methods use the same acronym, they are easily confused, but have different objectives for students.
Project-Based Learning
Project-based learning is learning that is organized around a project (Bell, 2010). It is a student-centered approach to learning, where students choose their topic of study and design an integrative project around the topic (Bell, 2010; Astawa et al., 2017). This form of study promotes self-efficacy in the learning environment. Such self-efficacy enables students to perform more difficult tasks and develop confidence in their abilities (Shin, 2018). These abilities generally help students to transfer their skills to the real world.
The Need to Rethink Assessments in the Age of Generative AI
The rapid advancement of generative artificial intelligence (genAI) technologies has sent shockwaves through the education sector, sparking intense debates about academic integrity, assessment practices, and student learning (Roe et al., 2023; Rudolph et al., 2023; Susnjak & McIntosh, 2024; Swiecki et al., 2022; Yeo, 2023). Since the public release of ChatGPT in November 2022, educators have grappled with concerns about cheating and the potential erosion of traditional academic values (Gorichanaz, 2023; Sullivan et al., 2023). However, as our understanding of genAI capabilities evolves, so too must our approach to assessment and teaching (Lodge et al., 2023).
Beyond Traditional Grades: Alternative Assessment Methods
As educators navigate the challenges and opportunities presented by generative AI (genAI), many are reconsidering traditional assessment approaches. Alternative assessment methods offer innovative ways to evaluate student learning that go beyond conventional grading systems, focusing on authentic learning, skill development, and meaningful engagement. These approaches not only address the challenges posed by AI but also align with research on effective learning and motivation (Furze, 2023; Pitts Donahoe, 2023).
Implementing Metacognitive Strategies in Online Learning Environments: Personal Experience Insights
In our Personal Experience Insights series, members of the Everspring Learning Design department share first-hand accounts of creating online learning content and meaningful takeaways from their professional experiences.
Quiz Writing Best Practices
Quizzes are one of the most common forms of assessment. Instructors can use quizzes to not only test students but also check on their students’ progress throughout a course. When used effectively, quizzes can assess students in a variety of ways. This piece will provide recommendations and best practices for enhancing the quality of quiz content.
10 Key Considerations for Online Course Development
Designing and delivering effective online courses requires careful consideration of numerous factors. As a result, it can be difficult to determine where to begin in the process, particularly for course developers and instructors who are new to online learning. This piece presents a curated list of resources aligned with 10 key considerations applicable across academic disciplines and degree programs.