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Establishing educational equity in computer science with generative AI and UDL will cause ripple effects across all content areas.

Advancing educational equity with UDL and generative AI


Taking computer science, with its long history of exclusion, towards an inclusive future will cause ripple effects across all content areas

Key points:

As we all struggle down the path toward true educational inclusion, we are confronted with four pillars of equity as described by Rochelle Guiterize: Access, Success, Power and Identity.

Educators with a mind towards equity typically excel at access. Opening doors to all students is an obvious move. However, we must continually push systems so that all students are able to be successful in spaces where they have ownership and feel a sense of belonging (identity). Otherwise, equity and inclusion are still just a dream.

While we recognize that some of these elements require large systems change, we also want to challenge all computer science educators to be the example.Taking computer science, with its long history of exclusion, towards an inclusive future will cause ripple effects across all content areas. Utilizing the AiiCE tenets, which recommend taking approaches that are responsive to student identities (Alliance for Identity-Inclusive Computing Education, 2023) we will suggest steps towards inclusive education pedagogy with Universal Design for Learning (UDL) and generative AI thought partners. 

A first step towards inclusive education can be done through the adoption of UDL. According to the CSTA: Inclusive Teaching Pedagogies, “UDL is an instructional planning approach designed to give all students an equal opportunity to learn by removing barriers that prevent students from fully engaging in their classroom communities” (White, 2023). However, this is a time-consuming (though worthwhile) task for already taxed teachers. 

In the frame of working smarter, not harder, we will describe a way to start integrating UDL principles into lessons, moving toward greater equity and inclusion through the use of Generative AI (GenAI) tools. The generative model being used is ChatGPT 3.5 (for optimum use we recommend ChatGPT 4). 

Teaching to the average student has never been effective. Our students possess a wide range of different brains, with different sensory and processing abilities. Good teachers are finding ways to meet the learning needs of all of these diverse brains within the same classroom.

UDL uses fundamentals from neuroscience to give educators a framework to empower all learners (CAST, 2018). UDL is a process, not a product, and requires that teachers rethink their planning and delivery of instruction. Though this is not necessarily asking for teachers to do more, it is absolutely asking them to do something different. As teachers wrestle with transforming their teaching practice, generative AI offers robust opportunities. When we pair a tested, research-based framework like UDL with AI, it brings us a step closer to the goal of true inclusion of all learners in CS classes.

Implementation of UDL requires rethinking the development and planning of lessons. Ralabate (2016) gives us five fundamental questions that allow teachers to begin to transform their practice. As teachers embrace this transformation, generative AI can be a thought partner in utilizing the five fundamental questions efficiently. These questions are around the accessibility, flexibility, lack of bias, validity, and reliability of our learning activities.

We address the first four of these questions below, along with generative AI prompts that can be used to increase the velocity of implementing each of these questions.

QuestionDescriptionGenerative AI Prompt
AccessibleWho can participate in the lesson and who can not?Please examine this lesson plan and tell me what type of student would be unable to fully participate in this lesson. 
FlexibleStudent choice in how they learn and how they demonstrate learning. Please provide multiple methods for students to demonstrate [learning target/objective].
Free of BiasWhat in my learning activity is inadvertently disadvantageous to students?What components of this lesson assume similar prior knowledge to me, the instructor, or what components are…..
ValidDoes my assessment evaluate the specific learning objective I am attempting to assess?Please change the reading level of this question to a 7th grade level (choose a level that is accessible to all students)

The final question is around reliability. Reliability measures the ability for a learning activity to meet its goals. Is the variability in my student’s performance due entirely to their performance, or is there variance that is due to the design of the activity (Ralabate, 2016). It is impossible to truly eliminate variance due to design, but it will be minimized if the first four questions are carefully considered and implemented into the design process. As a final check for reliability, GenAI can be used for triangulating grading – ask it to evaluate student data against a rubric. By comparing multiple GenAI responses with results from the teacher, we can minimize implicit bias, and ensure that the grades we are giving are authentic measures of student learning.

Systems produce what they are designed to produce. Our education system was constructed to produce inequitable outcomes, and that is what it produces. We believe that computer science educators can rise to the challenge of the day and remake their instruction in a way that effectively educates every brain–brains that come with extremely diverse needs. We know the why (equity), we know the how (UDL), and with generative AI, we now have the means to accomplish what is demanded of the moment.

References

Alliance for Identity-Inclusive Computing Education (2023). AIICE IIC Tenets. https://identityincs.org/resources/aiice-iic-tenets/

CAST (2018). UDL and the learning brain. Wakefield, MA. Retrieved from http://www.cast.org/products-services/resources/2018/udl-learning-brain-neuroscience

Gutiérrez, R. (2011). Context matters: How Should We Conceptualize Equity in Mathematics Education?. In Equity in Discourse for Mathematics Education: Theories, Practices, and Policies (pp. 17-33). Dordrecht: Springer Netherlands.

Ralabate P. (2016). Your UDL Lesson Planner: the Step-By-Step Guide for Teaching All Learners. Brookes Publishing.

White, S. V., et al. (2023, June 5). Inclusive Teaching Pedagogies. Computer Science Teachers Association. https://csteachers.org/inclusive-teaching-pedagogies/ 

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