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Artificial Intelligence as a Convergence of Disciplines: Why America Needs the U.S. Department of Education to Focus on Science-Technology-Engineering-Mathematics (STEM)

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Carolecameroninge

11 min read

Jan 11, 2025

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by Carole Cameron Inge, Ed.D.

Artificial intelligence (AI) is a quintessential example of how diverse disciplines intertwine to create transformative technology. It thrives at the intersection of literary arts, mathematics, science, technology, social science, and qualitative and quantitative methodologies:

  1. Literary Arts and Linguistics:
    • Natural Language Processing (NLP), a critical component of AI, relies heavily on linguistic theory, semantics, and storytelling to enable computers to understand and generate human-like text, passages and images.
    • Narrative structures influence how AI systems like chatbots, digital assistants and intelligent tutoring agents interact with humans, drawing from creative storytelling and ethnographic traditions. Images and video are created by “meta tags” used to organize data, making it easier to search, sort and retrieve assigned objects.

  2. Mathematics:
    • AI algorithms are rooted in linear algebra, calculus, probability, Bayesian and other statistical methods and frameworks. These mathematical principles enable pattern recognition, prediction and ultimately optimization.
    • Neural networks mimic brain functions borrowed from neuroscience using equations and advanced statistical methods.

  3. Science and Technology:
    • AI benefits from the scientific method for hypothesis testing and experimentation.
    • Fields like neuroscience inspire machine learning technology architectures, such as neural networks modeled from how the human brain is perceived to work. Neuro-physics principles like the Free Energy Principle (FEP) is just one example of how neural networks may inform new AI developments and new theories of application (Friston, 2024).

  4. Social Science:
    • AI incorporates behavioral and cognitive psychology, sociology, and mixed-methods economics to understand user behavior, ethics, norms, anomalies and societal impact.
    • Human-computer interaction (HCI) blends psychology, behaviorism, visualization, brain-based theories and design to enhance usability and human experiences.

  5. Qualitative and Quantitative Integration:
    • Qualitative insights, from ethnography and story formation inform AI design by understanding cultural and social contexts.
    • Quantitative methods validate AI’s performance through statistical analysis and numerical modeling.

Breakthroughs in artificial intelligence often emerge when distinct fields — once viewed as separate — combine their creative intellect with scientific precision. In my more than three decades as an AI researcher, developer, educator, and cognitive scientist, I witnessed firsthand this phenomenon in my laboratory just outside Washington, D.C. in the early 1990s, while working for Intelligent Automation, Inc. In 1992, DARPA, along with several NIST and NIH experienced researchers assembled with their unique expertise in computer science, mathematics, physics, education, and cognitive science to create the first artificial intelligence learning algorithms for military personnel training systems. While working with Department of Defense Education Activity (DoDEA) these experts joined forces, and the collaborative forcefield ignited a major AI breakthrough in U.S. education and training systems under a little-known program called Computer-Assisted Education and Training Initiative (CAETI). This $80 million program, funded by Defense Advanced Research Projects Agency (DARPA) in the early 1990s, was the first major largescale pioneering research for educational AI algorithms and stands as a powerful testament to the value of interdisciplinary collaboration, giving rise to STEM and the need for integrated research, learning and instruction. It is this experience I base the premise that the U.S. Department of Education should not be terminated, rather re-engineered to align with U.S. STEM innovation culture and values, the same that created AI initially.

The CAETI program developed by DARPA, the American arm of innovation for the U.S. military was a legendary program that included more than forty high profile research teams from around the world with diverse backgrounds and together they invented cutting-edge artificial intelligence (AI), simulation technologies, and multimedia systems to revolutionize education and training. Here’s an overview of its goals, implementation, and outcomes.

Goals of CAETI

1. Modernize Training for Military and Civilian Use: The program sought to advance training for both defense personnel and, eventually, broader educational applications.

2. Leverage Emerging Technologies: It focused on integrating AI-driven tutoring systems, virtual simulations, and interactive learning platforms.

3. Improve Efficiency and Effectiveness: The initiative aimed to reduce the time and cost of traditional training methods while enhancing learning outcomes.

Key Features

• Adaptive Learning Systems: AI-driven tutors tailored instruction to individual learners, providing customized feedback and progression paths.

• Immersive Simulations: Virtual reality and simulation environments allowed users to practice skills in realistic, risk-free settings.

• Collaborative Platforms: Tools were developed to enable remote cooperative learning, both in classrooms and distributed environments as teams were located throughout the world at military installations and in companies who participated in the program.

Outcomes for American Education and Society

1. Technological Advancements:

• The program drove innovation in AI-based learning systems, many of which formed the foundation for modern intelligent tutoring systems used today.

• Simulations developed for military training were later adapted for medical, engineering, and vocational training.

2. Impact on Education:

• While the initiative directly benefited military training, it inspired educational technology in civilian contexts, promoting the integration of AI and adaptive learning in classrooms.

• The approach shifted focus from one-size-fits-all teaching to personalized learning, a concept now widely known as differentiated instruction but rarely used in schools due to its difficulty to implement, especially with the slow adoption of AI, often viewed as cheating by ill trained educators.

3. Broader Societal Influence:

• CAETI showcased the potential for technology to address skill gaps in the workforce, influencing policies on technology integration in education.

• It highlighted the importance of public-private partnerships in driving innovation for societal benefit. Intelligent Automation, Inc., was one of several private partners in this epic DARPA program.

Limitations and Challenges

• Scalability Issues: Many of the advanced tools developed under CAETI were resource-intensive and difficult to scale for widespread adoption in public schools and the programs like CAETI hit the “AI Winter” extending from late 1980 into the early 1990s when funding for AI took a decline due to unmet expectations and what this author would call, the dark side of being on the bleeding edge of innovation. Furthermore, limited computing power and chip development speeds lagged the algorithmic magic that AI promised, leaving researchers with unmet ability to scale their models, over promising and underdelivering AI’s hope and dreams for zealots that knew its promise for education. Policy makers were left disappointed because frankly, AI was ahead of its time and not yet commercial ready as seen by the rapid adoption of ChatGPT by Open AI in 2022, 30 years later after the fact.

• Digital Divide: The program inadvertently highlighted disparities in access to advanced technology, raising concerns about equity in education. This inequity was further exaggerated by lack of broadband in rural parts of the U.S. during the 1990s and into 2000 and highlighted disparities in STEM programming with affluent areas seeing more benefit from STEM education and rural areas significantly less.

• Implementation Gap: While CAETI set the stage for future educational advancements, especially in AI, immediate impacts on public education were limited due to scarce infrastructure (broadband) and funding constraints.

DARPA’s Education Legacy

CAETI laid the groundwork for AI-driven educational tools and simulations that are now ubiquitous in training programs, online learning platforms, and educational software. Its vision of personalized, technology-enhanced learning continues to shape how education and workforce development are approached in the United States but a more unified approach from federal leaders is needed. Now that the “AI winter” is far behind us, America needs to take a second look at its structure of education in the U.S. and help school leaders realize that businesses require more U.S. STEM graduates. To make America great once more and to maintain its competitive AI edge, American needs a national focus and a solid plan to graduate and employ more STEM American workers.

By centralizing discovery within the U.S. Department of Education, America can remain at the forefront of innovation and maintain its competitive edge in the evolving landscape of AI, robotics and quantum computing. CAETI serves as a timely reminder for federal leaders to eliminate “siloed” approaches in U.S. education.

STEM Education in Silos in the U.S.

One significant barrier to producing skilled “U.S. workers” is that science, technology, engineering, and mathematics (STEM) disciplines in the U.S. school systems use an outdated “siloed” approach to teaching and learning, where disciplines are taught in isolation rather than as interconnected fields. K-12 schools have based its entire education system on 19th Century (Industrial Revolution) teaching methods. In this model, content areas are taught by “subjects,” an archaic approach borne from its development in the late 1800s and remains the standard school model today. Thankfully by the 20th Century, subjects like biology, physics, and chemistry became core parts of high school curricula but these disciplines remain taught separately, ignoring the fact that new developments in innovation work in “systems” and not independent but rather interdependent.

Problems with Siloed Approaches to School Curricula
1. Lack of Integration:
• Science, technology, engineering, and mathematics (STEM) are often taught as discrete subjects without demonstrating their interdependencies.
• For instance, engineering applications of math or the coding required for scientific experiments are rarely emphasized in school curricula. If we relied on these dated school models, AI would never have been borne.

2. Focus on Theory Over Application:
• Many STEM courses prioritize theoretical knowledge rather than hands-on, cross-disciplinary problem-solving, a much harder methodology to teach, often used by the most ambitious revered teachers.
• In recent interviews with Elon Musk about education design, he emphasized starting with “real-world problems,” such as designing sustainable technologies (self-driving cars and rockets that land autonomously), require integrating physics, engineering, mathematics and environmental science — a connection often missing in today’s K-12 classrooms.

3. Limited Interdisciplinary Exposure:
• Students rarely see how skills like programming (technology) apply to data analysis (math) in environmental research (science) or how engineering principles rely on physics and chemistry.

4. Testing and Standards are more than a century old and drive fragmentation (Horace Mann, 1840):
• Standardized testing rose in the 1900s, often assessing isolated skills rather than interdisciplinary understanding or real-world problem-solving.

Consequences of Siloed STEM Education

  1. Skill Gaps: Students graduate with fragmented knowledge that doesn’t prepare them for interdisciplinary fields like AI and robotics, where math, technology, social science and human factors converge.

  2. Lack of Creativity: A siloed approach discourages the kind of creative, integrative thinking that drives innovation. Take for example, MIT that created the Center for Advanced Visual Studies in the late 1960s and later The Media Lab, now seen as exemplary places of innovation and student creative engagement because their cultures are cross-disciplined, yet K-12 schools in America remain siloed in teaching and learning, a 100-year-old model!

  3. Reduced Appeal of STEM: Students fail to see the relevance of these subjects to their lives, making STEM careers less appealing, harder and more rigorous sounding to young minds periled by broken homes and new peer pressures created by modern social media.

Why the Siloed Approach is an Issue

The siloed nature of education in the U.S. contrasts sharply with the interdisciplinary demands of fields like AI, robotics, biotechnology, and climate science.

AI’s Demand for Holistic Integrated STEM Thinking:
The development of AI relies on blending technical programming skills with understanding societal impacts, ethical considerations, and effective communication. Siloed education doesn’t train students to think across these domains.

Global Competition:
Countries like China and India emphasize interdisciplinary STEM education, alongside vocational training, producing a more prepared workforce for emerging technologies and this is why they could soon catch up to the AI revolution and likely surpass the USA in its quest for superintelligence, creating cyber intellectual warfare we have yet to realize. Both nations are heavily investing in the capabilities for machines to surpass the collective known knowledge on the planet, far surpassing the intelligence of the smartest humans combined. This is an arms race that will halt all other AI work once achieved with winners and losers.

In fact, some of the leading U.S. based executives, to include President-elect Trump, Elon Musk, and Vivek Ramaswamy have emphasized the need to continue and promote America’s use of H-1B visas, a non-immigrant work visa that allows U.S. employers to hire foreign workers in “specialty occupations” requiring highly specialized knowledge and sighting lack of skilled STEM workers based in the U.S. The H-1B visa is often used to garner roles in STEM fields such as engineering, IT, biotechnology.

But is our American education system doing all it can to create better integrated STEM education in the United States? Are we making significant enough strides in integrating artificial intelligence (AI) and robotics into the K-12 systems and community college curriculum? It is clear the field of education needs a major facelift to stay relevant and accessible, especially if Silicon Valley feels they need to important STEM talent from our global competitors using the H-1B Visa program. Organizations like the National Science Foundation (NSF), Carnegie Mellon University’s Robotics Institute, and the U.S. Department of Education’s “YOU Belong in STEM” initiative have laid a very basic foundation. From the NSF’s 25 AI Research Institutes to Carnegie Mellon’s pioneering robotics research and the Department of Education’s focus on inclusive and emerging technologies, these efforts showcase the potential of STEM education to adapt but more is needed, and it can start with the U.S. Department of Education organizing around STEM innovation. Industry leaders like Microsoft, Google and Apple must also step into this policy conversation, promoting STEM literacy through innovative tools and resources. Industry must invest in the next generation of American-based talent.

However, the fragmented nature of these initiatives highlights a need for a unified, modern approach to education that resonates with today’s students and the nature of AI. Programs like those offered by the National Robotics Education Foundation (NREF) aim to inspire the next generation, but STEM still lacks a fresh, engaging integrated identity to captivate young minds and foster diverse participation. To truly transform, education the federal United States Department of Education must move beyond regulatory policy policing around equity and inclusion and embrace a future-forward STEM-focused policy framework, leveraging AI and robotics to build a dynamic, inclusive, and accessible learning environment for all students. AI is ultimately the educational equalizer but unless the United States takes a centralized approach to the knowledge wars, we will find ourselves in a space race once more where foreign nationals are continuing to threaten the democratization of education in this country and this time it won’t be Russia but China or India who beats us beyond the Moon to Mars. Mr. President, without a national agency focused on STEM education, cyber warfare will take place in space and Musk, alone can’t save us. Don’t dismantle an agency that has the potential to lead the STEM cognitive war for America’s youth and the next generation of inventors and scientists. Wake up America, there is tons to do.

Inge is a cognitive scientist who holds a doctorate from the George Washington University in education technology policy. She worked closely with the U.S. Department of Education, NASA, DARPA, NIH, the Virginia Department of Education, Longwood University and was founder of the Virginia Tech Modeling and Simulation Center of Excellence. In this capacity, Inge was a founding board member of the Mid-Atlantic Broadband Cooperative, along with Virginia Tech’s Rector, and this work directly materialized into Microsoft Corporation investing $2 Billion in Southside Virginia. She also contributed to the development of AI while working at Intelligent Automation, Inc. for DARPA’s CAETI program in 1992.

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