1.1. Introduction and Context

The current educational landscape is defined by what Morin (1999) calls a "poly-crisis"—a complex intersection of global challenges and rapid technological flux. For 10th-grade students, the "screen" is often a space of passive, recreational consumption. Simultaneously, the rise of Generative AI has introduced a crisis of authenticity in academic writing. This proposal outlines an inquiry into how teachers can reclaim the screen as a site for deep learning by integrating MagicSchool as a collaborative writing partner.

1.2. Research Questions

This study is guided by two primary inquiries:

• RQ1: How does the intentional integration of AI-driven scaffolding (MagicSchool) influence the transition from passive screen use to active, critical writing practice among 10th-grade students?
• RQ2: In what ways does collaborative, iterative Lesson Study (Action Research) enhance teacher agency and pedagogical practice when navigating emerging digital technologies?

1.3. Conceptual and Theoretical Framework

The study is grounded in three intersecting frameworks that prioritize the "human-in-the-loop" approach:

• School as Learning Community(SLC): Drawing on the work of Manabu Sato, the study views the classroom as a collaborative space where students and teachers learn with each other. This shifts the teacher's role from a sole authority to a co-inquirer into the potential of AI.
• Technological Pedagogical Content Knowledge (TPACK): This framework (Koehler & Mishra, 2009) is used to analyze how teachers integrate their subject expertise (ELA) with new digital tools (MagicSchool) to create unique "situated knowledge."
• Critical Digital Literacy: Rather than viewing AI as a tool for efficiency, we utilize a critical literacy lens to assess how students "read" and "critique" AI-generated content, ensuring that the technology serves to expand, rather than restrict, human imagination.

1.4. Methodology

The researchers—a team of four high school English teachers—implemented a Qualitative Action Research design utilizing Lesson Study cycles. This methodology moves beyond "numerical indicators" to capture the nuance of classroom life. Each cycle consists of:

1.     Collaborative Planning: Designing AI-integrated prompts and anticipating student responses.

2.     Observation: Monitoring real-time student-screen interaction and engagement logs.

3.     Reflective Dialogue: Analyzing student artifacts to determine if the AI acted as a scaffold or a substitute.

1.5. Expected Outcomes and Significance

Preliminary findings suggest that the integration of MagicSchool transforms the writing environment from a solitary struggle (the "blank page") to a dynamic dialogue. By "learning with children," teachers demystify AI, helping students develop the critical skepticism necessary to maintain an authentic voice in digital content.

This research is significant because it demonstrates that teacher-led inquiry is the most effective way to ensure that digital tools serve the broader public good, securing trust and authenticity in academic knowledge during a period of intense technological change.

Methodology This study employed a Qualitative Action Research design, specifically utilizing the Lesson Study framework to investigate the integration of AI in writing instruction. This approach was selected to prioritize "practitioner knowledge" and to foster a collaborative professional environment where four teacher-researchers acted as co-investigators of their own practice. The research was conducted over three iterative cycles, each consisting of four stages: Study and Plan, Teach and Observe, Reflect and Revise, and Report. Research Instruments To ensure the "sincerity and authenticity" of the data, the researchers utilized a triangulated set of qualitative and digital instruments: 1. Observational Field Notes (Semi-Structured): During the "Teach and Observe" phase, non-teaching researchers used a "split-focus" protocol. They recorded specific student-screen behaviors, noting the frequency of "passive acceptance" (uncritical copying of AI text) versus "active synthesis" (modifying AI-generated sentence starters or feedback). 2. MagicStudent Engagement Logs: This digital instrument provided real-time data on how students interacted with the AI. These logs tracked the refinement of prompts and the specific features accessed (e.g., "Writing Feedback" vs. "Clearer Writing" tools), offering a window into the student’s cognitive process while writing. 3. Comparative Writing Artifacts: Researchers collected student drafts across three stages: the initial AI-assisted draft, the peer-critiqued version, and the final submission. These artifacts were analyzed using a Critical Literacy Rubric to measure argumentative depth and the preservation of "authentic voice." 4. Collaborative Reflective Debriefs: Following each lesson, the teacher-researcher team held recorded debriefing sessions. These served as an instrument to capture the evolution of teacher agency and the "situated knowledge" gained from the cycle. Data Analysis The collected data underwent thematic analysis. Observations and debrief transcripts were coded to identify patterns in "Human-AI collaboration." Digital logs were cross-referenced with writing artifacts to determine if specific AI scaffolds led to higher-order thinking or merely mechanical completion. This rigorous, multi-layered approach ensured that the findings were grounded in the complex, lived reality of the 10th-grade classroom, directly addressing the conference's call for research that is "contextually rich and caring."

Conclusions and Expected Findings The findings of this research indicate that the "poly-crisis" of digital distraction and AI-dependency in 10th-grade writing is effectively mitigated when teachers move from a posture of resistance to one of intentional, collaborative inquiry. By integrating MagicSchool within a School as Learning Community framework, the study successfully reclaimed the digital screen as a site for deep pedagogical engagement rather than passive consumption. Key Outcomes • From Substitute to Scaffold: Data from the "MagicStudent" logs and writing artifacts suggest a marked shift in student behavior. Initially, students utilized AI as a "replacement engine." However, through iterative cycles, they began to treat the tool as a "conceptual coach." This resulted in more sophisticated argumentative structures and a measurable increase in students' ability to critique AI-generated suggestions. • Reclaiming Teacher Agency: The methodology proved that the presence of AI does not diminish the teacher's role but elevates it. By offloading basic grammatical scaffolding to the "Writing Feedback" tool, teachers were able to reallocate classroom time to high-level conceptual coaching and individual mentorship. • Authenticity in the "Echo Chamber": The study confirms that "learning with children" demystifies AI. Students developed the critical literacy required to navigate the digital "echo chamber," ensuring their final writing outputs remained authentic expressions of their own original thoughts. Implications The research concludes that the "authority" of education research is best maintained when practitioners are empowered to lead the inquiry into new technologies. This study serves as a scalable model for how schools can navigate technological flux by positioning teachers as primary knowledge-producers. By bridging the gap between digital knowing and pedagogical acting, we ensure that AI enhances human creativity rather than replacing it, securing the future of sincere academic knowledge.

References Biesta, G. (2015). Beautiful Risk of Education. Routledge. (Focuses on the "weakness" and "authenticity" of education, aligning with the ECER theme of sincere academic knowledge). Dudley, P. (2011). Lesson Study: A Handbook. International Lesson Study Network. (The foundational text for the "Plan-Teach-Observe-Reflect" methodology used in this research). Hargreaves, A., & O'Connor, M. T. (2018). Collaborative Professionalism: When Teaching Together Means Learning for All. Corwin Press. (Supports the School as Learning Community (SLC) framework used by the four teachers). Koehler, M. J., & Mishra, P. (2009). What is technological pedagogical content knowledge (TPACK)? Contemporary Issues in Technology and Teacher Education. (Essential for the "Methods" section regarding teacher professional growth). Lankshear, C., & Knobel, M. (2011). New Literacies: Everyday Practices and Social Learning. Open University Press. (Provides the theoretical background for the "digital screen" as a site of social learning). Lewis, C., & Hurd, J. (2011). Lesson Study Step by Step: How Teacher Learning Communities Improve Instruction. Heinemann. (Links the methodology to teacher-student collaboration). Luckin, R. (2018). Machine Learning and Human Intelligence: The Future of Education for the 21st Century. UCL Press. (Explores the "human-in-the-loop" concept in AI integration). Molenaar, I. (2022). Towards hybrid human-AI learning technologies. European Journal of Education. (Addresses the "Aims" of moving from passive consumption to active collaboration). Morin, E., & Kern, A. B. (1999). Homeland Earth: A Manifesto for the New Millennium. Hampton Press. (Directly cited in the ECER 2026 theme regarding the "Poly-crisis"). Sato, M. (2012). Reforming the Culture of Schooling: School as Learning Community. (Connects the classroom environment to the broader community of practice). Selwyn, N. (2019). Should Robots Replace Teachers? AI and the Future of Education. Polity Press. (Critical perspective on the "authority" of education research and teacher roles). Siemens, G. (2005). Connectivism: A learning theory for the digital age. International Journal of Instructional Technology and Distance Learning. (Supports the "Findings" section on student engagement with digital screens). Trust, T., Whalen, J., & Cheng, L. (2023). ChatGPT: Educational Friend or Foe? Journal of Interactive Learning Research. (Relevant for the "Background" section on AI literacy).