A 21st-Century Interdisciplinary Approach to Reading Education
A 21st-Century Interdisciplinary Approach Leveraging AI and Technology to Enhance Reading Levels in the Philadelphia School District
Abstract
The School District of Philadelphia continues to face significant literacy challenges, with only 17% of fourth graders reading proficiently and just 31% of students in grades 3–8 meeting expectations in 2023–24. This article proposes a conceptual framework integrating artificial intelligence and interdisciplinary curriculum design to improve outcomes. Grounded in research and SDP’s PASS initiative, the model emphasizes AI-driven personalization, cross-curricular literacy, technology-enhanced instruction, and teacher empowerment while addressing equity, privacy, and human connection.
Keywords: Artificial Intelligence in Education; Interdisciplinary Curriculum; Literacy Development; Technology-Enhanced Instruction; Urban Education
Introduction
The School District of Philadelphia (SDP) faces persistent challenges in literacy achievement. According to the 2024 NAEP Trial Urban District Assessment, only 17% of fourth graders scored at or above Proficient in reading, compared with 33% nationally (NCES, 2024). Winter 2023–24 benchmark data showed that 31% of students in grades 3–8 were performing on grade level, representing a modest 2% increase from the prior year (WHYY, 2024). These data underscore a widening gap between Philadelphia students and national expectations.
This article addresses the question: How can interdisciplinary curriculum design, enhanced by artificial intelligence and digital technologies, improve reading outcomes in Philadelphia schools? The purpose is to propose a conceptual framework grounded in emerging research on AI-enabled personalized learning, cross-disciplinary literacy integration, and technology-supported instruction, with particular attention to teacher professional development and educational equity.
Framework Development
This article employs a conceptual synthesis rather than an empirical study. Sources informing the framework include:
- Student achievement data from national and local reports (NCES, 2024; WHYY, 2024).
- Case studies and pilot research on AI-supported literacy instruction (Kosmas et al., 2025; Zhao et al., 2025; Schiavo et al., 2021; Thaqi et al., 2024).
- District initiatives, notably SDP’s Pioneering AI in School Systems (PASS) professional development program (Penn GSE & SDP, 2024).
These sources were analyzed and synthesized into four strategic pillars that form the proposed framework.
Results
The synthesis produced four primary findings:
Personalized Learning Through AI: AI-powered platforms adapt instruction to individual student needs, predict learning difficulties, and provide targeted interventions (Kosmas et al., 2025). For students with dyslexia, AI reading assistants improved comprehension and engagement (Zhao et al., 2025; Thaqi et al., 2024).
Interdisciplinary Curriculum Design: Embedding literacy instruction across science, social studies, and mathematics fosters authentic application of reading skills. Generative AI supports text leveling and nonfiction comprehension within disciplinary contexts (Sutton & Lee, 2024).
Technology-Enhanced Literacy Instruction: Digital platforms encourage collaborative reading and writing. Attention-driven read-aloud technology improved comprehension by 24% among struggling readers (Schiavo et al., 2021).
Teacher Empowerment Through Professional Development: The PASS program equips educators with the technical and pedagogical knowledge necessary to integrate AI effectively (Penn GSE & SDP, 2024). By reducing administrative burdens and supporting differentiated instruction, AI allows teachers to prioritize relationships and individualized support.
Key Implications
The integration of AI, interdisciplinary curriculum design, and digital tools has strong potential to improve literacy outcomes in Philadelphia. Key implications include:
- Closing literacy gaps: AI-enabled personalization addresses variability in student needs.
- Equity: Device access and connectivity remain barriers; equitable distribution is essential.
- Ethics and privacy: Implementation requires robust safeguards for student data.
- Teacher readiness: Professional development supports sustainable adoption.
- Human connection: AI should augment, not replace, the relational aspects of teaching.
While promising, this framework is conceptual. Future studies should implement pilot programs in Philadelphia classrooms to assess impact on state assessments, benchmark growth, and student engagement.
Conclusion
Philadelphia faces urgent literacy challenges but also has opportunities to lead nationally in AI-enhanced, interdisciplinary education. By adopting a framework centered on AI-driven personalization, cross-curricular literacy, technology-rich instruction, and empowered educators, SDP can address systemic barriers to proficiency. Pilot implementations and empirical studies will be critical to validate effectiveness and scalability.
Acknowledgments
Thank you to the School District of Philadelphia educators and the University of Pennsylvania Graduate School of Education for their innovative efforts in advancing AI integration in literacy instruction. I also gratefully acknowledge the American College of Education’s Integrated Curriculum M.Ed. program for providing the education and guidance necessary to understand and implement these strategies.
References
Kosmas, P., Nisiforou, E. A., Kounnapi, E., Sophocleous, S., Theophanous, G., & colleagues. (2025). Integrating artificial intelligence in literacy lessons for elementary classrooms: A co-design approach. Educational Technology Research and Development. https://doi.org/10.1007/s11423-025-10492-z
National Center for Education Statistics. (2024). 2024 Reading Trial Urban District Snapshot Report: Philadelphia—Grade 4. U.S. Department of Education. https://nces.ed.gov/nationsreportcard/subject/publications/dst2024/pdf/2024220XP4.pdf
Pennsylvania School District & University of Pennsylvania Graduate School of Education. (2024). Pioneering AI in school systems (PASS). Penn GSE. https://www.gse.upenn.edu/professional-development/custom-offerings/pioneering-ai-school-systems-pass
Schiavo, G., Mana, N., Mich, O., Zancanaro, M., & Job, R. (2021). Attention-driven read-aloud technology increases reading comprehension in children with reading disabilities. arXiv. https://arxiv.org/abs/2103.05296
Sutton, S., & Lee, S. (2024). How generative AI can support nonfiction reading. Edutopia. https://www.edutopia.org/article/how-generative-ai-can-support-nonfiction-reading
Thaqi, E., Mantawy, M., & Kasneci, E. (2024). SARA: Smart AI Reading Assistant for reading comprehension. arXiv. https://arxiv.org/abs/2404.06906
WHYY. (2024). Philadelphia public school students’ reading scores increase [News article]. https://whyy.org/articles/philadelphia-public-school-students-reading-scores/
- Zhao, S., Xiong, S. C., Pang, B., Tang, X., & He, P. (2025). Let AI read first: Enhancing reading abilities for individuals with dyslexia through artificial intelligence. arXiv. https://arxiv.org/abs/2504.00941
