STRIVE
Emerging considerations when designing assessments for artificial intelligence use
Authors: Lorelei Anselmo (MEd), Sarah Elaine Eaton (PhD), Raisa Jivani (MEd), Beatriz Moya (MA), Alysia Wright (PhD)
Last modified: February 21, 2024
Overview
This resource is for academic staff, post-doctoral scholars, and graduate assistants teaching (TAs) to learn more about how best to design and/or modify course assessments that permit students’ use of generative artificial intelligence (GAI) to complete their assignments in academic courses.
This document is a starting point for discussions and reflections to foster a deeper understanding of emerging considerations inherent in AI-based course assessments. These emerging considerations may serve as a roadmap to promote ethical, responsible, and beneficial use of generative artificial intelligence applications in course assessment practices. This document may strengthen understanding and engagement with the ethical dimensions of GAI assessments, promoting fairness and transparency in students’ educational experiences.
The STRIVE Model
The STRIVE: Emerging Considerations model builds on Smyth et al.’s (2011) and Squire’s (2018) 3E Frameworks with Laurillard’s (2002) considerations. The STRIVE model can be used to design assessments with GAI use in mind that support alignment between technology and approaches to learning in assessments to include student-centeredness, transparency, responsibility, integrity, validity, and equity (STRIVE).
Background
Smyth et al. (2011) developed the 3E Conceptual Framework: Enhance, Extend, and Empower to improve technology use to aid student learning. In 2018, Squires (2018) refined the 3E Framework to encourage instructors to highlight the purpose or intent of the learning task to determine the technology level it represents:
Additionally, Laurillard (2002) noted that some uses of technology can and should align with the learning they enable, such as creating efficiencies, improving accessibility and/or flexible timing, encouraging enriching interactions, and facilitating the development of key skills, abilities, and literacies.
Using GAI in assessments presents an opportunity to align the use of technology with learning to help students develop future-focused attributes and skills.
The STRIVE Model and Future-focused Student Skill Development
The STRIVE model incorporates the following considerations for assessments that support student use of GAI: student-centered, transparency, responsibility, integrity, validity, and equity.
Using the STRIVE model may lead to the development of the following future focused skills for students.
Using GAI in assessments encourages students to:
| Enhance | Extend | Empower | |
|---|---|---|---|
| S – Student-centeredness | Engage in flexible learning | Collaborate to problem solve | Commit to critical thinking |
| T – Transparency | Develop clarity in GAI application | Identify reliable sources | Dialogue about authorship |
| R – Responsibility | Be accountable for content creation | Recognize overreliance on GAI use | Examine and challenge GAI-produced content |
| I – Integrity | Engage in values-based discussions | Model appropriate use of GAI | Critique GAI-generated output for accuracy and bias |
| V – Validity | Demonstrate learning in fair and equitable ways | Build agency through ethical decision-making | Develop meta-cognitive skills through self-reflection |
| E – Equity | Understand how to access GAI tools | Develop knowledge on the risks and benefits of GAI use | Recognize and advocate for equitable and inclusive GAI use |
This resource was designed as an invitation to reflect on key considerations for assessments using generative artificial intelligence. The STRIVE considerations of student-centeredness, transparency, responsibility, integrity, validity, and equity serve as a guide for assessment design that highlights a holistic approach to the use of artificial intelligence while promoting future-focused learning for our students.