Pennings, A.J. (2026, Sep 04) Combining AI with Technological Systems Management (TSM) to Create Integrated BS, MS, and PhD Degrees. apennings.com https://apennings.com/science-and-technology-studies/combining-ai-with-technological-systems-management-tsm-to-create-integrated-bs-ms-and-phd-degrees/

Introduction

In a previous post, I examined Science, Technology, and Society (STS) programs in major universities and how they were beginning to address developments in Artificial Intelligence (AI). This post examines how STS can be combined with Technological Systems Management (TSM) and sustainable development strategies to form a powerful university-level framework for investigating and preparing for an AI-intensive future.[1]

The STS-informed AI strategies emerging at leading institutions such as MIT’s Program in Science, Technology, and Society, Stanford’s STS Program and Human-Centered AI ecosystem, Harvard’s STS work at the Kennedy School, Berkeley’s critical and risk-oriented centers, Cornell’s Global AI Initiative, and Stony Brook’s new Department of Technology, AI and Society within the College of Engineering and Applied Sciences (CEAS) supply critical intellectual and research tools for understanding artificial intelligence as a sociotechnical system.

These strategies emphasize social construction, actor-networks, co-production of knowledge and social order, responsible innovation, power dynamics, equity, and contextual implementation. On their own, however, they risk remaining primarily analytical or critical. When deliberately combined with Technological Systems Management (TSM), the engineering-school tradition exemplified at Stony Brook CEAS that integrates systems thinking, technical competence, and management practice, the result is a powerful educational model. This combination can and should underwrite new or redesigned BS, MS, and PhD degrees that equip graduates with both deep intellectual understanding and practical capabilities in management, innovation, policy, and technical skills.

To maximize societal relevance and global impact, these degrees should be organized organized around a clear ICT4D (Information and Communication Technologies for Development) and AI for Good (AI4Good)/AI4D focus.

The Complementary Strengths

STS AI strategies excel at showing how social groups, institutions, values, and power relations shape AI systems; how agency and accountability are distributed across human and non-human actors; and how research agendas, technical designs, and societal outcomes co-produce one another. They train students to question hype, surface hidden assumptions, anticipate unintended consequences, and design more inclusive and legitimate approaches.

The video below interviews Brian Rosenberg, the former President of the prestigious Macalester College and Visiting Professor at the Harvard Graduate School of Education. He is also the Director and Senior Advisor at African Leadership University (ALU) and the author of “Whatever It Is, I’m Against It: Resistance to Change in Higher Education”.

Technological Systems Management contributes the complementary engineering, leadership, and managerial toolkits. These would include systems modeling and optimization, lifecycle analysis, project and program management, risk and reliability engineering, innovation processes, strategic decision-making, organizational change, value creation, and the ability to move from concept to deployed, maintained, and governed technological systems.

TSM programs have long drawn on the STS premise that complex technologies are “much too important to be left to the engineers alone,” precisely the insight that makes STS-TSM collaboration natural. Together they produce graduates who can:

– Analyze AI sociotechnically (STS intellectual core),
Design, integrate, and manage AI-enabled and enhanced systems at scale (TSM technical and systems core),
– Lead innovation processes and organizational adoption (management and innovation skills),
– Shape and navigate policy and regulatory environments (policy skills),
– And ground all of the above in rigorous technical and project planning understanding.

This is not simply adding “ethics modules” to engineering degrees or “tech literacy” to social-science degrees. It is a fused curriculum in which intellectual depth and practical competence reinforce each other at every level.

ICT4D (Information and Communication Technologies for Development) and AI for Good (AI4Good) Emphasis Strengthens the Model

Standard AI education often prioritizes performance benchmarks, commercial applications, or high-resource environments. An ICT4D / AI4Good/AI4D lens shifts the center of gravity toward solving problems that matter to underserved and low-resource communities, designing systems that function under constraints of infrastructure, data, skills, and governance, addressing global inequalities in access to AI benefits and exposure to AI risks, and ensuring that AI contributes to (rather than undermines) long-term social, economic, and environmental sustainability.

STS frameworks are particularly well-suited to this agenda. They surface issues of power, inclusion, local knowledge, data justice, and the social construction of “development” and “good.” Technological Systems Management supplies the complementary capacity to design, implement, scale, manage, and sustain complex technological systems in real organizational and institutional settings. Together, they produce graduates who can move pilot projects and “AI for Good” narratives into durable, context-appropriate, and accountable AI systems.

This orientation would embed a robust international perspective and a sustained commitment to sustainable development, aligning technical and managerial excellence with the challenges of equity, inclusion, resilience, and the UN Sustainable Development Goals (SDGs). Sustainable development applies to all nations in the age of climate concerns and involves explicitly mapping AI systems to SDG targets for adaptation, resilience, and mitigation applications.

Management, innovation, and policy skills remain central, including strategy and innovation management for development contexts, results-based management, risk and resilience, stakeholder engagement across cultures, and navigation of international policy and funding landscapes. Experiential learning is an important option to the traditional classroom with internshipes, international field projects, collaborations with organizations working in the Global South, and studios that treat social and environmental performance as core design requirements. SUNY Korea, for example, has worked closely with the Asia-Pacific Red Cross / Asia Pacific Disaster Resilience Centre (APDRC) and UN Office of Disaster Risk Reduction located in Songdo, South Korea.

Degree-Level Design

Bachelor of Science

An undergraduate TSM/STS-AI degree would give students foundational technical skills (programming, data systems, basic machine learning, network administration, systems engineering) alongside core STS frameworks applied to AI, introductory management and innovation courses, and policy literacy.

Capstone projects would require students to design or evaluate an AI system while producing both technical deliverables and sociotechnical analyses of stakeholders, risks, values, and implementation conditions. Graduates would be prepared for entry-level roles in AI product teams, systems analysis, responsible innovation units, or policy support positions, with the intellectual agility to grow into more strategic roles.

Master of Science

The MS level is ideal for deeper integration. Students would pursue advanced technical electives or concentrations (e.g., AI systems architecture, data infrastructure, human-AI interaction) while mastering STS analytic methods, technology strategy, innovation management, risk and resilience frameworks, and policy analysis. Applied projects, internships, or client-based work would demand the simultaneous use of technical, managerial, and critical skills. This degree would serve mid-career professionals and strong bachelor’s graduates seeking to lead AI initiatives in industry, government, NGOs, or international organizations—precisely the hybrid talent currently in short supply.

Doctor of Philosophy

A PhD would train researchers and future faculty who can advance both knowledge and practice. Dissertations would typically combine technical or systems contributions with STS-informed analysis of design processes, organizational embedding, policy implications, or societal impacts. Graduates would be positioned to conduct rigorous interdisciplinary research, shape AI research agendas, advise on strategy and policy, and educate the next generation.
Stony Brook’s existing PhD pathway in Technology, Policy, and Innovation, currently expanding toward Technology, AI and Society, offers a natural institutional home for such a degree.

Why This Combination Is Strategically Necessary

Purely technical AI degrees are producing graduates skilled at building models but underprepared for organizational realities, policy constraints, equity considerations, or long-term societal consequences. Purely STS or policy degrees can produce sophisticated critics who lack the technical fluency or systems-management skills needed to intervene effectively in design and deployment. The hybrid model closes this gap.

It aligns with the directions already visible at the institutions discussed earlier. Stanford and MIT demonstrate the value of pairing deep technical environments with STS insight. Harvard shows the power of linking STS to strategy and policy. Berkeley and Cornell illustrate global, justice-oriented, and risk-aware research strategies. Stony Brook’s engineering-school TSM tradition and its current expansion into Technology, AI and Society provide a concrete platform to fuse systems management, technical training, and STS at scale.

Employers, public agencies, and research funders increasingly seek professionals who can operate across these domains. Degrees that deliberately cultivate that combination will produce graduates who can formulate and execute AI strategies that are technically sound, managerially effective, politically informed, and socially robust.

This focused combination addresses a clear talent gap. International organizations, development agencies, governments in the Global South, NGOs, social enterprises, and responsible technology firms need professionals who possess technical fluency, systems-management competence, policy literacy, and the critical intellectual tools to avoid techno-solutionism and neo-colonial patterns in AI deployment. It also aligns with the distinctive strengths of the institutions already advancing STS AI strategies. Cornell’s Global AI Initiative explicitly prioritizes pluralistic and globally relevant AI. Berkeley’s justice-oriented work and Stony Brook’s engineering-school TSM platform (with its expanding AI-and-society mandate) provide natural institutional homes. MIT, Stanford, and Harvard contribute models of rigorous interdisciplinary research and training that can be adapted toward development and sustainability outcomes.

Conclusion

The decisive intellectual and practical contribution comes from uniting traditional STS analysis with Technological Systems Management and in the study of AI. This combination yields degrees that cultivate genuine understanding together with management, innovation, policy, and technical capabilities. International ICT4D and AI4D perspectives, along with sustained attention to AI’s entanglement with global USD structures and fintech systems, serve as valuable supporting emphases.[2] They broaden the empirical scope, raise the stakes of the analysis, and ensure relevance to pressing global challenges without displacing the foundational STS–TSM core. Programs designed on these principles would produce graduates capable of analyzing, designing, managing, and governing AI and related ICT systems with both critical depth and operational competence, whether those systems operate in commercial, public, developmental, or global-financial contexts. That integrated capacity is what the complexity of contemporary AI demands.

Summary

Combining Science and Technology Studies (STS) strategies with Technological Systems Management (TSM) creates an educational model that unites intellectual depth with practical capability. STS supplies the indispensable critical foundation and enabling framework for understanding artificial intelligence in its full social, ethical, and political complexity, while TSM provides the systems discipline, project planning, technical competence, and management leaderships required to act effectively on that understanding. Integrating these domains into coherent BS, MS, and PhD degrees is not merely desirable but represents a logical and necessary transition for contemporary higher education.

Anchoring these programs in an ICT4D and AI4Good / AI4D mission, with a strong international outlook and explicit commitment to sustainable development, makes the model more distinctive and more urgently needed. Attention to AI’s entanglement with global financial structures, fintech systems, and development contexts broadens the empirical scope and ensures relevance to pressing global challenges without displacing the foundational STS–TSM core.

Such programs would prepare a new generation of professionals and scholars capable of analyzing, designing, managing, innovating, and governing AI systems with both critical depth and operational competence. In an era when AI reshapes every sector, this integrated capability—uniting technical effectiveness, equitable design, and globally responsible governance—is precisely what universities, industry, and society require.

Notes

[1] Our undergraduate TSM degree at SUNY Korea has a specialization option in ICT4D. It was started as ICT for Sustainable Development in recognition of the efforts by former UN Secretary General Ban-ki Moon but was changed to the more academically and internationally recognized ICT4D a few years ago.
[2] Rolf Lidskog, Göran Sundqvist; When Does Science Matter? International Relations Meets Science and Technology Studies. Global Environmental Politics 2015; 15 (1): 1–20. doi: https://doi.org/10.1162/GLEP_a_00269
AI Prompt(s) Argue that STS AI strategies should combine with Technological Systems Management to create BS, MS, and PhD degrees that combine intellectual understanding with management, innovation, policy, and technical skills

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Not to be considered financial advice. AI is often used, and results are thoroughly interrogated. Links are used for some citations. Views are my own and do not express the stances of my employers, past or present.

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Anthony J. Pennings, PhD is a Professor at the Department of Technology and Society, State University of New York, Korea and a Research Professor for Stony Brook University. He teaches AI and broadband policy. From 2002-2012 he taught digital economics and information systems management at New York University. He also taught in the Digital Media MBA at St. Edwards University in Austin, Texas, where he lives when not in Korea.

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Category: artificial intelligence, science and technology studies, technology management
Tags: AI4Good > and Society Studies (STS) > ICT4D > Science > Science - Technology - and Society Studies (STS) > Technology > TSM

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