8 Student Evaluation Practices and Assessment Strategies
AI is going to drastically change how faculty perceive assessment and grading (Young, 2023), requiring them to rethink learning outcomes, redesign assignments (Stanford, 2023), and also consider more progressive approaches to student learning instead of more traditional methods.
This statement from CJ Yeh (Fashion Institute of Technology Professor of Communication Design Foundation) and Christie Shin (Fashion Institute of Technology Associate Professor of Communication Design Foundation) describes some of the changes that will need to be made within the field of design education:
We will need a greater focus on interdisciplinary collaboration. In order to solve the increasingly complex problems that contemporary society is facing today, it is critical for aspiring designers to learn how to collaborate effectively with developers, engineers, and other stakeholders. This means students will need to communicate effectively, share ideas, and work together to achieve common goals. Some key learning objectives would include the following:
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- Critical thinking and problem framing: AI can accomplish many tasks, but it cannot replace creativity, critical thinking, and (most importantly!) empathy. Students need to learn how to use these skills to accurately define problems and come up with new solutions.
- Cloud-based remote collaboration: These tools are essential for designers who want to work efficiently and effectively with team members who are located in different places and other fields. Designers can share files, communicate in real time, and track progress on projects from the comfort of their own homes or offices.
- AI-assisted design process: Students need to learn how to use AI technologies, including using AI to automate tasks, generate ideas, and test designs.
- Ethics and social responsibility: We must stop focusing on simply teaching students how to create the most persuasive ads, seductive designs, addictive games, etc. The next generation of designers needs to learn about the ethical implications of design and social responsibility. This includes learning about privacy, accessibility, and sustainability.
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AI’s Impact on Summative Assessment: An Example
In an Alchemy webinar titled “Harnessing the Power of AI: Transforming Assignments and Assessments in Higher Education, Dr. Danny Liu (University of Sydney) discussed the importance of designing authentic assessments (Villarroel et al., 2017) and the importance of feedback (Carless & Boud, 2018).
The Villarroel et al. study suggests that faculty make assessment more like real-world tasks students might encounter in a future job. Students tend to learn better, feel more motivated, and feel like they are managing their own learning. The study suggests a step-by-step model to help faculty create their own authentic assessments in higher education.
Carless and Boud discuss student feedback literacy, which is how students are able to understand and use feedback to improve their work and learning. The paper focuses on how students respond to feedback and some challenges they face when applying feedback. Carless & Boud offer two activities that can help students improve their feedback literacy: giving feedback to each other and analyzing examples of good work.
Dr. Liu suggests a Two-Lane Approach in regard to assessment strategies with all of this in mind: how it’s important to have some kind of “Lane 1” (read: traditional assessment to ensure learning outcomes are being met) approach, but how “Lane 2” would factor in the authentic assessment that students would be more motivated to complete. He uses this example in his presentation to demonstrate the approach:
Table 1
Lane 1: Assurance of Learning Outcomes | Lane 2: Human-AI Collaboration |
Short term:
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Short term:
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Longer term:
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Longer term:
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The idea is to try to find balance between traditional assessment methods and new ways to assess student learning by encouraging their collaboration with AI. Dr. Liu provided an example from a marketing class.
Table 2
Example of a Two-Lane Approach
Learning outcomes: apply marketing strategy concepts in real-world scenarios; demonstrate communication skills; evaluate the effectiveness of different strategies.
Lane 1: Assurance of Learning Outcomes | Lane 2: Human-AI Collaboration |
Live Q&A after in-class presentation (defend research/analysis, etc.)
Giving students unseen case study in a live unsupervised setting. |
Bing Chat for market research and competitor analysis
Adobe Firefly for campaign design Collaboration process is documented (fact-checking, improving, critiquing) In-class presentation Process heavily weighted |
In this example, the Lane 2 approach has more components as well as several opportunities for interaction with AI technology. Bing Chat is an AI-powered search engine, Adobe Firefly is an AI that can generate images, and students would have the opportunity to use other AI tools that could help generate text.
Process plays a big role in Dr. Liu’s scenario (see the process book assignment in the next section), and there’s more at stake for students in the Lane 1 assessment.