So, How Do You Build Connection With 200 Students?
From Chronic Absenteeism to AI-Powered Relational Infrastructure in Secondary Education
Jordan B. Smith Jr., Ed.D.
CEO, Annapolis Creed LLC
Former Mathematics & Computer Science Teacher
Amazon Future Engineer Teacher Ambassador
Abstract
Secondary schools across the United States continue to face rising chronic absenteeism, declining mathematics achievement, disengagement, and increasing teacher burnout. Unlike elementary teachers who may work closely with 20–30 students daily, secondary educators often serve 150–200 students across multiple class periods, creating structural barriers to meaningful relationship-building. This article argues that connection can no longer depend solely on teacher personality, charisma, or unsustainable emotional labor. Instead, schools must develop systemic relational infrastructure capable of scaling communication, personalization, engagement, and support. Drawing from research on school connectedness, chronic absenteeism, mathematics achievement, culturally responsive teaching, trauma-informed education, and artificial intelligence systems literacy, this paper proposes the AI-Connected School Framework as a model for scalable human-centered educational systems. The article further argues that educators must transition from passive users of educational technology to designers of intelligent communication systems that support students and families at scale. Implications for district leadership, mathematics education, teacher preparation, and workforce readiness are discussed.
Introduction
The Central Question of Secondary Education
So how do you build a connection with 200 students?
This may now be the defining question of modern secondary education.
Schools across the United States continue to struggle with chronic absenteeism, declining mathematics performance, emotional disengagement, staffing shortages, and increasing teacher burnout. Although schools continue implementing intervention programs, attendance meetings, accountability structures, and remediation efforts, many indicators suggest that current approaches are not producing sustainable improvement. Chronic absenteeism rates remain elevated following the COVID-19 pandemic, and mathematics achievement continues to lag behind international benchmarks (National Assessment Governing Board [NAGB], 2025; Organization for Economic Cooperation and Development [OECD], 2023).
At the center of this crisis is a structural challenge often ignored in educational reform discussions: relational scale.
Elementary teachers frequently work with the same group of students throughout the school day, allowing them to build familiarity, emotional consistency, and strong family communication systems. Secondary educators, however, may teach 150–200 students daily across fragmented schedules and short instructional periods. Under these conditions, meaningful connections become increasingly difficult to sustain.
The challenge is not whether relationships matter. Research consistently demonstrates that school connectedness is associated with improved academic outcomes, mental health, attendance, and behavioral engagement (Centers for Disease Control and Prevention [CDC], 2024). The deeper issue is whether secondary schools are structurally designed to support connection at scale.
This article argues that the future of secondary education depends upon moving beyond personality-driven relationship models toward systemic relational infrastructure supported by communication systems, AI-enhanced personalization, culturally responsive teaching, and human-centered workflow design.
The Relational Crisis in Secondary Education
Chronic Absenteeism and Emotional Withdrawal
Chronic absenteeism has emerged as one of the most significant educational challenges of the post-pandemic era. Attendance Works (2024) reported that chronic absenteeism nearly doubled during the pandemic years, affecting millions of students nationwide. However, absenteeism is not merely an attendance issue. It is often an emotional, relational, and identity-based issue connected to disengagement from school itself.
Attendance Works (2024) identified disengagement, aversion to school, barriers to attendance, and misconceptions about the importance of attendance as major contributors to chronic absenteeism. These findings suggest that many students are not simply refusing to attend school out of defiance; rather, they may no longer perceive school as meaningful, supportive, or connected to their future.
Research on school engagement reinforces this concern. Fredricks et al. (2004) described engagement as multidimensional, including behavioral, emotional, and cognitive components. Students may physically attend school while remaining emotionally detached and cognitively disengaged. Likewise, emotionally disconnected students are more likely to avoid participation, withdraw socially, and eventually disengage academically.
The CDC (2024) identified school connectedness as a protective factor strongly associated with student well-being and long-term success. Students who believe adults in school care about them are more likely to demonstrate improved attendance, resilience, and academic persistence. Yet secondary schools often struggle to operationalize connectedness within systems designed primarily around scheduling efficiency and content delivery.
The 7th Grade Breakdown and Mathematics Decline
One of the most critical transition points in education occurs during middle school, particularly around seventh grade. Research examining middle school transitions found that students frequently experience declines in academic performance, motivation, and school engagement during this developmental stage (Schwerdt & West, 2012).
The transition into middle school often coincides with:
This breakdown becomes especially visible in mathematics.
Unlike many disciplines, mathematics is cumulative and conditional. Students cannot successfully build advanced mathematical understanding without a strong conceptual foundation. Fractions support proportional reasoning. Proportional reasoning supports algebraic thinking. Algebra supports higher-order modeling and problem solving. When foundational understanding collapses, future learning becomes unstable.
This creates a dangerous cycle. Students who repeatedly experience failure in mathematics often develop mathematical anxiety, avoidance behaviors, and negative academic identities. Over time, students may begin to view mathematics not as a skill to improve, but as evidence of personal inadequacy.
National and international assessments reinforce the urgency of this issue. The OECD (2023) reported that U.S. students scored below the OECD average in mathematics on the Programme for International Student Assessment (PISA) 2022. Similarly, NAEP mathematics results remain below pre-pandemic levels, particularly in middle grades (NAGB, 2025).
The mathematics problem is therefore not merely instructional. It is emotional, relational, and systemic.
Mathematics, Connection, and Productive Struggle
The National Council of Teachers of Mathematics (NCTM, 2014) emphasized that effective mathematics teaching requires meaningful discourse, conceptual understanding, purposeful questioning, productive struggle, and student-centered reasoning. These instructional practices inherently depend upon relational trust.
Students are unlikely to engage in productive struggle if they fear humiliation, embarrassment, or judgment. Mathematical discourse requires emotional safety. Students must believe that mistakes are part of learning rather than evidence of failure.
Hattie et al. (2017), in Visible Learning for Mathematics, emphasized the importance of teacher clarity, feedback, classroom discussion, and collective teacher efficacy in improving student outcomes. Importantly, these influences depend heavily on the quality of relationships between students and educators.
Collective teacher efficacy is particularly important because it shifts the conversation away from individual heroism toward systemic responsibility. The solution to student disengagement cannot depend solely upon charismatic teachers working beyond exhaustion. Schools must create systems in which adults collectively support students' connection, communication, and academic identity.
From Mainframes to AI Agents
A Historical Perspective on Workforce Readiness
The current AI transition in education mirrors earlier technological transitions that reshaped workforce expectations.
As a former Marine officer educated at the United States Naval Academy before the rise of personal computers and the internet, I experienced computer science education during the era of mainframe systems and teletype terminals. Students learned programming through command-line interfaces connected to computers we could not physically see. The machine itself often delivered assignments, evaluated programs, and provided automated feedback.
Looking back, these early systems resembled primitive versions of today’s AI-assisted environments.
What was missing at the time, however, was ownership and experimentation. Students interacted with centralized systems but could not freely build, modify, or continuously explore them.
The arrival of personal computers changed that experience completely. For the first time, computing became personal, experimental, and persistent. I purchased my own computer and continued working with technology daily, while many others avoided it. During my military service, I developed attendance and tuition assistance programs that were later adopted by the government. Those experiences reinforced an important lesson: systems can solve organizational problems at scale.
Today, education faces another systems-level challenge:
Current intervention models continue producing inconsistent results. Yet schools often continue repeating the same structures while expecting different outcomes.
Sugata Mitra and Self-Organized Learning
Educational researcher Sugata Mitra demonstrated the power of curiosity-driven learning through the “Hole in the Wall” experiments. Mitra placed internet-connected computers into underserved communities in India without formal instruction and observed how children organized themselves to explore, learn, and collaborate independently.
The results were remarkable. Children taught themselves computer navigation, problem-solving, language acquisition, and collaborative learning behaviors (Mitra, 2010).
Mitra’s findings suggest that access to powerful systems, combined with the freedom to experiment, can unlock extraordinary learning potential. This insight has profound implications for AI literacy in education.
AI Literacy and the Future Workforce
Programs such as Amazon Future Engineer were developed in response to workforce concerns about access to computer science, computational literacy, and future technological demands. The initiative reflects a growing recognition that future careers will increasingly involve automation systems, artificial intelligence, cloud infrastructure, and computational problem solving.
The future workforce will not simply use software.
Workers will increasingly:
This creates a new educational imperative:
Teachers themselves must become systems literate.
Historically, technology integration in schools often focused on isolated tools:
The AI era requires something deeper:
This distinction is critical. Using AI tools is fundamentally different from understanding AI systems.
The AI-Connected School Framework
The central argument of this article is that schools must move toward relational infrastructure capable of scaling human connection without losing humanity.
The AI-Connected School Framework proposes that districts develop systems where artificial intelligence supports communication, personalization, and engagement while preserving teacher-centered relationships.
The framework includes four major pillars.
Pillar 1: Personalized Communication Systems
AI-enhanced communication systems can support:
These systems allow schools to maintain relational consistency across large student populations.
Pillar 2: Teacher AI Assistants
If I were a school superintendent today, I would purchase an enterprise AI workflow platform and provide every teacher with an individual innovation sandbox. Teachers should be able to build:
Teachers learn systems best when they can build, experiment, fail safely, and share innovations collaboratively.
Pillar 3: Teacher Innovation Ownership
Teachers should retain ownership of the systems they build.
When educators retire or change districts, they should be able to preserve their workflows, prompts, communication systems, and innovations as portable intellectual capital. This transforms teachers from passive consumers of software into designers of educational systems.
Pillar 4: Human-Centered AI
Artificial intelligence should not replace teachers.
Instead, AI should:
The goal is not automation for efficiency alone. The goal is scaling support while preserving relationships.
Implications for District Leadership
From Compliance Systems to Innovation Systems
Educational leaders face mounting pressure to improve attendance, mathematics performance, graduation rates, and workforce readiness while simultaneously addressing staffing shortages, teacher burnout, and increasing mental health concerns among students. Yet many districts continue to rely on organizational structures designed primarily for compliance, efficiency, and reporting rather than for relational engagement and innovation.
Traditional district infrastructures emphasize:
While these systems provide important organizational functions, they rarely address the deeper relational dimensions of student engagement. Research on school connectedness demonstrates that students are more likely to attend school, persist academically, and demonstrate resilience when they feel known, valued, and supported by adults within the school environment (CDC, 2024).
This creates a critical leadership challenge:
Can districts redesign systems that scale human connection without overwhelming educators?
The AI-Connected School Framework proposes that district leaders begin to view AI not merely as a software tool but as relational infrastructure capable of supporting communication consistency, personalization, and engagement at scale.
Research on chronic absenteeism suggests that disengagement frequently develops long before students become chronically absent (Attendance Works, 2024). Early intervention systems supported by AI-assisted communication workflows may help districts identify disengagement patterns before students fully disconnect from school.
If implemented thoughtfully, AI-supported systems could help districts:
Importantly, the framework does not advocate replacing teachers with automation. Rather, it advocates reducing repetitive operational burdens that often prevent teachers from investing time in meaningful human interaction.
This perspective aligns with Hattie’s (2009) findings regarding collective teacher efficacy. Sustainable school improvement depends less upon isolated heroic educators and more upon systems that enable educators to work collectively and effectively.
District leadership must therefore shift from a mindset of technological adoption toward one of organizational redesign.
The central question for district leaders becomes:
Will schools continue building compliance systems, or will they intentionally build innovation systems?
Innovation systems create environments where educators can:
Districts that fail to develop innovation infrastructure risk widening the gap between workforce realities and classroom experiences.
Implications for Mathematics Education
Relevance, Identity, and Systems Thinking
The mathematics crisis in American education cannot be separated from questions of engagement, relevance, and student identity.
For decades, students have asked mathematics teachers:
“When will I ever use this?”
Artificial intelligence, automation systems, data science, and computational modeling may now provide one of the clearest answers educators have ever had.
Modern AI systems rely heavily upon mathematical concepts, including:
Yet traditional mathematics instruction often isolates procedural fluency from real-world application and technological relevance. Students frequently experience mathematics as disconnected from meaningful future opportunities. This disconnection contributes to a decline in mathematics identity and disengagement, particularly during middle school transitions (Schwerdt & West, 2012).
The AI era changes this conversation.
Mathematics classrooms now have an opportunity to become laboratories for:
This shift aligns closely with the recommendations of the National Council of Teachers of Mathematics (NCTM, 2014), which emphasized reasoning, problem solving, discourse, conceptual understanding, and productive struggle as central elements of effective mathematics instruction.
Similarly, Hattie et al. (2017) emphasized that visible learning in mathematics depends upon clarity, feedback, discourse, and teacher-student relationships. These findings reinforce the article’s central argument that mathematics achievement is deeply connected to relational trust and emotional safety.
Students who believe they are “bad at math” often disengage, not because of inability, but because repeated failure has damaged their mathematical identity. Research on culturally responsive teaching further suggests that students engage more deeply when instruction reflects relevance, identity, and meaningful context (Gay, 2018; Ladson-Billings, 1995).
Therefore, improving mathematics outcomes may require schools to redesign not only instruction, but also the emotional and relational experiences surrounding mathematics learning.
The integration of AI systems literacy into mathematics education may provide students with a clearer understanding of why mathematical thinking matters in the future workforce.
Implications for Teacher Preparation Programs
Preparing Educators for the AI Era
Teacher preparation programs were largely designed for an educational model in which teachers delivered content, managed classrooms, and used technology primarily for instructional support.
The AI era requires a fundamentally different conception of teaching.
Future educators will increasingly need competencies in:
This does not mean every teacher must become a software engineer. However, teachers may increasingly need to understand how intelligent systems shape communication, workflow automation, personalization, and learning environments.
Historically, educational technology preparation focused on:
The next phase of educational technology requires teachers to become:
Programs such as Amazon Future Engineer reflect the growing recognition that computational literacy is becoming foundational across industries. Workforce readiness increasingly depends upon problem-solving, computational reasoning, systems thinking, and technological adaptability (OECD, 2023).
However, workforce readiness cannot remain isolated within computer science classrooms alone.
All educators may need some level of AI systems literacy because intelligent systems are increasingly influencing:
Teacher preparation programs must therefore expand beyond isolated instructional technology courses to broader systems-literacy frameworks that prepare educators for AI-supported educational environments.
This recommendation aligns with broader calls for transformational leadership and innovation-centered educational reform to prepare students for rapidly changing workforce demands (Hattie, 2009; OECD, 2023).
Limitations and Ethical Considerations
Human-Centered AI in Education
Although AI-supported relational infrastructure offers significant potential, important ethical concerns must also be addressed.
Educational AI systems raise legitimate questions involving:
Schools must avoid treating students as data points to be optimized rather than human beings to be understood.
Research on trauma-informed education emphasizes that students require emotional safety, trust, and authentic human connection to engage fully in learning (Hammond, 2015). Excessive automation risks undermining those relational foundations if implemented carelessly.
The AI-Connected School Framework, therefore, emphasizes human-centered implementation.
AI should:
AI should not:
Additionally, districts must recognize the digital divide that continues to affect many communities. AI-supported systems require thoughtful implementation strategies that ensure equitable access for students and families with varying levels of technological connectivity.
Ethical implementation also requires transparency. Students, parents, and educators should understand:
Research on culturally responsive education further suggests that technological systems must remain sensitive to issues of culture, identity, language, and equity (Gay, 2018; Hammond, 2015).
Ultimately, the goal of AI integration in education should not be efficiency alone.
The goal should be preserving humanity within increasingly complex educational systems.
That is why the central principle of this framework remains:
AI should scale human connection—not replace it.
Conclusion
The future of secondary education may not depend solely upon curriculum reform, accountability systems, or additional intervention programs.
It may depend upon whether schools can redesign themselves around a scalable human connection.
Secondary schools were largely built for industrial efficiency. Students, however, learn through relationships, identity, relevance, and a sense of belonging.
Artificial intelligence now offers educators a historic opportunity:
not to replace human connection,
But to scale it.
The schools that thrive in the future may be those that successfully combine:
into environments where students feel:
The central question is no longer whether AI belongs in schools.
The real question is:
Will schools use AI to deepen human connection—or continue operating systems that make connection impossible at scale?
References
Attendance Works. (2024). Stemming the surge in chronic absence: What states can do. Attendance Works.
Centers for Disease Control and Prevention. (2024). School connectedness helps students thrive. U.S. Department of Health and Human Services.
Donohoo, J. (2017). Collective teacher efficacy: How educators’ beliefs impact student learning. Corwin.
Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School engagement: Potential of the concept, state of the evidence. Review of Educational Research, 74(1), 59–109.
Gay, G. (2018). Culturally responsive teaching: Theory, research, and practice (3rd ed.). Teachers College Press.
Hammond, Z. (2015). Culturally responsive teaching and the brain. Corwin.
Hattie, J. (2009). Visible learning: A synthesis of over 800 meta-analyses relating to achievement. Routledge.
Hattie, J., Fisher, D., Frey, N., Gojak, L. M., Moore, S. D., & Mellman, W. (2017). Visible learning for mathematics, grades K–12. Corwin.
Ladson-Billings, G. (1995). Toward a theory of culturally relevant pedagogy. American Educational Research Journal, 32(3), 465–491.
Mitra, S. (2010). The hole in the wall: Self-organizing systems in education. TED Conferences.
National Assessment Governing Board. (2025). 10 takeaways from the 2024 NAEP results. U.S. Department of Education.
National Council of Teachers of Mathematics. (2014). Principles to actions: Ensuring mathematical success for all. NCTM.
Organization for Economic Cooperation and Development. (2023). PISA 2022 results. OECD Publishing.
Osterman, K. F. (2000). Students’ need for belonging in the school community. Review of Educational Research, 70(3), 323–367.
Schwerdt, G., & West, M. R. (2012). The impact of alternative grade configurations on student outcomes through middle and high school. Journal of Public Economics, 97, 308–326.
Souers, K., & Hall, P. (2016). Fostering resilient learners: Strategies for creating a trauma-sensitive classroom. ASCD.
World Economic Forum. (2023). The future of jobs report 2023. World Economic Forum.
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