In order to center teachers’ voices in the design and implementation of culturally and linguistically sustaining (CLS) AI literacy learning experiences in a localized context, my research commits to engaging with teacher participants in both the co-design process and building their capacity through a collectivist and inclusive professional development unit (PDU). As an educator and researcher committed to equity, accessibility, and our local communities, I created a conceptual framework entitled “CLS AI Literacy”.
The CLS AI Literacy conceptual framework is built around three intersecting pillars of pedagogy (co-constructivism, socioculturalism, and critically humanizing intersectional feminism) each with their own practical applications that bridge pedagogical theories into action. To support co-constructivism and socioculturalism, embodied and experiential learning as well as universal design for collectivist learning approaches encircle them. To support the critically humanizing intersectional feminist pillar of this framework, as well as to acknowledge my positionality as a white privileged researcher in culturally and linguistically diverse spaces, I include digital Black feminism (Benjamin, 2019; Knight Steele, 2021) and Indigenous AI protocol (Lewis et al., 2020) as supporting approaches. Each of these approaches to learning bridge the concepts into practical application, and guide learning in the PDU, design of the research process, and how this research gets shared with other communities to inspire CLS AI literacy for all learners to thrive.
The participant sample for this study consists of approximately 10-20 PK-12 teachers across Denver Public Schools. Participants are eligible for this research if they enroll in a 45-hour DPS Professional Development Unit (PDU) focused on designing and implementing culturally and linguistically sustaining (CLS) AI literacy lessons. Enrollment in the PDU is completely voluntary and outside of the normal scope and contract hours of a teaching contract. The course will be taught by Kali Peracchia in the fall of 2025, and offered to all teachers in Denver Public Schools through broad and neutral communication channels such as Schoology curriculum group bulletin boards and district newsletters. Students enrolled in the course will be invited to participate in the study, but they will not be required to participate in the study. All students — participants and non-participants — will receive the same curriculum during the course; this study does not change the existing PDU curriculum.
Purposive stratified sampling will be used to narrow the larger PDU group (approximately 50-100 students) down to 10-20 research participants, aiming for representation across grade levels (i.e., PK-12), content areas (e.g., humanities, math, science) and participant demographics (e.g., race, gender, linguistic identity) to align with the goals of the study. The sample reflects maximum variation, capturing a broad spectrum of teacher experiences, positionalities, and classroom contexts. Ultimately, diversity is critical to the study’s use of phenomenography, which seeks to analyze the qualitatively different ways teachers conceptualize and enact CLS AI literacy through design-based approaches.
Instruments
Transcriptions of Live PDU Session Recordings: To capture the process of design-based approaches to data collection and participant engagement, live PDU sessions will be recorded and transcribed, removing any input from PDU learners not participating in the research process.
Lesson Planner and Implementation Reflection Template (Appendix A): Participants will complete three iterations of this lesson planner and reflection template to guide discussions, next cycle of co-construction, and future design of PDU module content.
Semi-Structured Interview Questions (Appendix B): Participants will engage in a semi-structured interview after implementation of lessons to explore conceptions of CLS approaches to AI literacy curriculum.
Post-PDU Survey (Appendix C): Similar to the pre-survey, this post-survey will yield Likert-style self-evaluation scores of AI literacy, open-ended questions regarding CLS approaches to AI literacy, and feedback/recommendations for how the district proceeds with AI literacy curriculum design in the future.
Analysis Plan
Data (lesson plan artifacts, guided reflections, and semi-structured interviews) collected through a design-based approach will be analyzed using a phenomenographic methodology that measures the qualitative difference in teachers’ perceptions around culturally and linguistically sustaining (CLS) AI literacy curriculum design and practices. Participants’ qualitative data will be coded using language from a CLS instructional walkthrough tool DPS utilizes (Appendix D). The coding process and analysis for qualitative data follows a four-stage model (Bowden et al., 1992) where themes are identified, categorized, and organized on a spectrum of sophistication.
In phenomenographic tradition, data will then be analyzed and presented in an outcome space, where qualitative data around a phenomenon (CLS AI literacy curriculum design and pedagogical practices) are quantified and organized hierarchically based on a level of sophistication (Åkerlind, 2017). This process of data transformation allows qualitative differences of teachers’ conceptions of CLS AI literacy to be quantified, organized, and compared across other quantifiable measures to target differentiated professional learning based on a teacher’s current state (Creswell & Creswell, 2023). Thus, two examined features (curriculum design and pedagogical practices) will be used to structurally categorize a spectrum of conceptions increasing in sophistication similar to categories in a phenomenographic outcome space designed by Yau and colleagues (2022) (Appendix E). In coding quantitative data for further analysis, acknowledgement of the general conception is noted with a (✓) and numbered based on multiple mentioning; a (⛤) notes the highest frequency, or main conception, expressed by each teacher; and a (❈) notes the highest level, or most sophisticated, participant conception.
Findings will be presented through integrating quantitative trends with rich, detailed qualitative data collected from lesson plan artifacts, participant reflections, and semi-structured interviews. These findings will be presented in order of sophistication as modeled by Yau and colleagues (2022), starting with a more surface-level understanding of the phenomenon (CLS AI literacy) and moving towards a deeper understanding. A phenomenographic approach allows for an analysis of trends in participants’ perceptions while also providing nuanced insights into practical applications of CLS AI literacy in DPS classrooms.
Expected results:
1. Detailed description of variation in teacher conceptions: Teachers will demonstrate a spectrum of understandings about culturally and linguistically sustaining (CLS) AI literacy, from surface-level applications to more sophisticated, justice-centered approaches. This variation will highlight opportunities for differentiated professional learning.
2. Teacher-driven innovation: Participants are expected to generate original, AI-infused lesson designs that embed universal design and CLS principles. These lessons will serve as concrete artifacts of teacher agency and innovation.
3. Framework replicability: Findings aim to illustrate how design-based research (DBR) cycles can be replicated across contexts to scale equitable AI literacy practices. This includes adaptable tools such as lesson templates, reflection guides, and coding frameworks.
4. Building professional capacity: Teachers will presumably report increased confidence and capacity to integrate AI literacy in ways that affirm student identities and assets. Their reflections will provide insight into how equity-driven PD supports sustained shifts in practice.
5. Contribution to the field: The outcome space will map how teachers’ conceptions of CLS AI literacy evolve over time, offering new theoretical and practical contributions to AI in education research. These findings will extend beyond DPS, providing guidance for districts and researchers globally.
First, this study responds to a gap in literature that is predominantly focused on techno-centric approaches to developing students’ AI literacy in computer science courses, adding a culturally and linguistically sustaining lens to interdisciplinary approaches to AI literacy in K-12 schools. Also, this study is intentionally designed for replicability, offering scalable solutions (e.g., design-based instruction, continuous professional learning model, alignment to instructional framework) that can be transferred and adapted across classrooms, schools, districts, and even larger professional organizations. Another aim of this study is to push the field of research forward by engaging with participants in collective effort towards more effective and inclusive technology integration into schools. Last, this study provides nuanced detail into practical tools, curriculum design conjectures, and implementation techniques to support all learners in building AI literacy to thrive.
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