Technology, People, and Policy Certificate

This course examines the development and impact of technology within historical, social, and cultural contexts, drawing on theories from Science and Technology Studies (STS). Students will explore how technology shapes and is shaped by society through frameworks like Actor-Network Theory, Technological Determinism, and the Social Construction of Technology. Using case studies and critical analysis, the course explores the relationship between technology, politics, culture, power, and identity, while also considering the ethical, societal, and political implications of technological advances. By the end, students will gain a deeper understanding of how technologies evolve within broader socio-cultural forces.

How is technology shaping our social and emotional lives? How do our emotions and social interactions shape technology? And can AI ever truly understand what we are feeling? These are some of the mysteries that we will seek to unravel in this interdisciplinary course. We will discuss foundational scientific theories and the latest empirical studies, combine lectures with small-group discussions, and experiment, write, collaborate, and imagine together.

This graduate-level course explores the relationship between technology, psychology, and education across different cultural contexts. Students will examine how technology influences learning, cognitive development, and educational systems, considering cultural values, practices, and societal structures. Topics include the impact of digital technologies on education, cultural variations in educational tools, and the psychological effects of technology-mediated learning. The course includes comparative studies from both Western and non-Western perspectives, highlighting how culture shapes the integration of technology in education. Through case studies and research discussions, students will learn how to use technology in culturally responsive ways to enhance education and psychological well-being.

In today’s information-rich world, data is generated at an unprecedented scale, offering both opportunities and challenges. How do we navigate this overwhelming volume of data and leverage it to shape better, more equitable decisions, policies, and programs? In this course, students will tackle pressing policy challenges through case studies and readings, while developing and/or honing their skills along the stages of the data science lifecycle — data preparation, analysis, interpretation, visualization, and communication. The course also examines the ethical dimensions of data use, exploring how issues of power, bias, privacy, consent, and representation influence the way data shapes decisions and policy outcomes.

You'll explore key concepts and challenges in global technology policy, focusing on how technology impacts societies, economies, and political systems worldwide. Students examine issues like data privacy, cybersecurity, AI ethics, digital sovereignty, and the digital divide, with attention to both developed and developing nations. The course also covers the roles of international organizations, governments, and multinational corporations in shaping tech policy. Through case studies and comparative analysis, you'll learn how different countries approach tech regulation and the barriers to equitable policy development. By the end, you'll be well-prepared to engage with the global challenges and opportunities posed by technological advancements.

This practical course provides you with the tools and strategies to navigate the complexities of data governance. Through case studies, hands-on activities, and expert discussions, you'll explore key frameworks, ethical issues, and regulatory requirements in data management across industries. You'll learn best practices for ensuring data privacy, integrity, and compliance, as well as how to develop policies for responsible data use. By the end, graduates will be prepared to tackle data governance challenges and help create effective and fair tech policies.

This graduate seminar aims to introduce students to the core substantive issues and key social science authors in the interdisciplinary study of adult development and aging, drawing mainly from the disciplines of human development, psychology, sociology, education and the cognitive sciences. The course will focus on the substantive issues of (1) personality and self, (2) cognition and learning, (3) generativity and social commitment, (4) love and family, and (5) work and retirement. Across these five domains, we will pay careful attention to the dynamics of continuity and change across the life course and the socio-historical context within which developing lives are situated.

Federal AI policy in the United States has swung between administrations and remains stalled in Congress, and international regulation varies sharply across jurisdictions. In the meantime, U.S. states have become the most active AI regulators in the world. State legislatures introduced more than a thousand AI-related bills in a single recent session. In December 2025, the Trump administration issued an executive order specifically targeting state AI laws, which is itself evidence that the federal government now treats states as the binding constraint on AI.

This course walks students through that landscape. It opens with the international and federal context, including the sharp divergence between the current and previous administration. It then turns to how states are wrestling with AI, from the high-level frameworks down to specific statutes, vetoes, agency guidance, and executive orders. From Week 4 onward, the course is built around four issue areas where state activity is most concentrated, with each week studying one issue comparatively across multiple states.

The course holds two framings of state activity in tension throughout. The first is states as laboratories of democracy, generating policy experiments the federal government can later learn from. The second is states as a costly patchwork, leaving companies guessing and compliance officers exhausted. It is honest about the limits of state regulation, and about what only the federal government or international institutions can do. It closes by asking what the state-led era means, and could mean, for the future of AI.

Research and policy aimed at the goal of improving children's lives require an in-depth understanding of how and why children develop the way they do. This course is designed to provide an intensive overview of the science of child development, with an emphasis on young children. A broad range of theoretical perspectives from a variety of fields will be emphasized. Students will come away with an understanding of the key factors in healthy child development, defined to include socioemotional, cognitive and physical well-being. In addition, we will apply the scientific knowledge of child development to several major policy and programmatic issues, such as early childhood education, antipoverty programs, marriage promotion and health disparities. This course also highlights the role of context: family, peers, childcare, school, employment, neighborhood and policy environments. Ethical considerations as well as measurement (i.e., the tools of science) will be stressed. In addition, a multidisciplinary perspective is a central theme.

Technology today in the form of computational tools and materials has great influence on most aspects of our lives whether we know it or not. Yet many of the possibilities for computational tools to be used to transform and improve experiences of learning and teaching have yet to be realized. In this course, we will explore views of learning rooted in inquiry-based and justice-oriented pedagogies that can help to realize the promises and avoid the pitfalls that technological innovations can hold for learning in both informal and formal settings. The explorations in this course are grounded in both the present and the past of research, design and development of digital tools with learning and teaching in mind (i.e. programming/coding, robotics, social media, VR/AR, games, digital design fabrication, and AI). We will look to the past and examine the present in ways that will help us to contribute to futures where more possibilities are realized.

This course focuses on the exploration, construction and analysis of multi-agent models. Sample models from a variety of content domains are explored and analyzed. Spatial and network topologies are introduced. The prominent agent-based frameworks are covered as well as methodology for replicating, verifying and validating agent-based models. We use state of the art ABM and complexity science tools.

This course is a hands-on practicum in designing and building technology-enabled curricula and learning environments. We will use many rich software toolkits designed to enable novice computer-users to get their “hands dirty” doing iterative software design. In addition to the hands-on component, the course is also designed to introduce you to the Constructionist Learning design perspective. This perspective, first named by Seymour Papert and greatly influenced by the work of Jean Piaget, is very influential in the learning sciences today. The Constructionist approach starts with the assumption that teaching cannot successfully proceed by simply transferring knowledge to students’ heads. Skillful teaching starts with the current state of knowledge of the student. In order for students to learn effectively, they need to construct the knowledge structures for themselves. In the spirit of Constructionism, we will engage in our own construction of artifacts in this class and, through this activity, explore and evaluate the design of kits and tools intended to enable learners to construct their own motivating and powerful artifacts. We will do this by constructing both physical and virtual artifacts and by engaging in reflective discussion of both the artifacts themselves and the tools used to construct them. In the final project, students will put all of this together by designing and implementing a constructionist learning environment.

Classrooms, work settings, and family relations are hotbeds of emotion. But what is an emotion? What happens in our bodies when an emotion is triggered? How can emotions help us live productive, healthy, and connected lives? And can we ever truly understand what somebody else is feeling? These are some of the mysteries that we will seek to unravel in this course. We will read literature from Darwin to the latest scientific studies, combine lectures and small-group discussions, conduct research experiments, and engage in peer review and online collaboration.

Introduction to programming practice using Python. Analysis and formulation of problems for computer solution. Systematic design, construction, and testing of programs. Substantial programming assignments in Python.

This introductory programming course is not part of the major. It provides an introduction to programming for those that can benefit from becoming better programmers, but without committing to the major student's version of the course.

This course offers a comprehensive introduction to project management and an examination of its synergies with organizational change. Fundamentally, project management executes an organization’s strategy through change. Project managers are responsible for planning and delivering projects to support that strategy. We’ll start by understanding how to translate strategic vision into a well-defined project scope. Then, we will delve into the roles of sponsors, managers, and team members, ensuring roles and responsibilities are aligned with the desired outcomes. We will also focus on the “must-do” project planning activities needed to facilitate organizational success. This course also will study best practices for evaluating project health, communicating progress with stakeholders, and supporting the team during delivery. The curriculum focuses on the strategic and practical application of project management best practices and tools to develop and implement robust project plans. By integrating principles of organizational change, you will learn how to manage projects that facilitate change adoption and agility, leading to organizational success.

The course highlights the importance of adapting to organizational contexts that require different approaches, such as incorporating the use of new technologies (e.g., AI), Agile and Lean Six Sigma methods. Additionally, this course covers project team leadership, risk management, effective communication, and establishing success metrics that include quality, budgetary, schedule requirements, customer satisfaction, and team engagement. By the end of the course, you will be equipped with the skills and knowledge to lead projects that not only meet the organization's goals but also drive strategic change.

This course serves as an introduction to the quantitative methods sequence and will cover applied statistical methods. The course will provide useful tools for students who intend to conduct their own statistical analyses, as well as those who want to become critical consumers of others' analyses. Topics to be covered include descriptive measures; basic probability; sampling and sample size estimation; hypothesis testing; techniques for analyzing categorical data; ANOVA; presenting and describing statistical results.

This course is intended to be a continuation of the quantitative methods sequence that begins with Quantitative Methods I. The course will cover applied statistical methods and will provide useful tools for students who intend to conduct their own statistical analyses, as well as those who want to become critical consumers of others' analyses. Topics to be covered include the use of data for descriptive and causal analyses, linear regression, experimental design, panel data methods, hierarchical linear models and instrumental variables.

This course provides an introduction to many of the important tools of estimating causal models, including experimental analysis, difference-in-difference, regression discontinuity designs, propensity score matching, event study, interrupted time series and other regression-based models. Emphasis is placed on understanding the rationales behind the empirical techniques chosen and the interpretation of the analysis. Students develop hands-on technical skills as well as critical research-reading skills in this course.

Recent research has demonstrated that, rather than human biology determining human behavior and experience, human biological development is exquisitely sensitive to environmental input, particularly the social environment. In this course, we will discuss theoretical models and data regarding the interplay between biology and social experience in development. Topics include gene-environment correlations, gene-environment interactions, epigenetics, and theoretical models for the interplay between biological systems and ecological conditions. We will learn some basic facts about brain development, stress physiology, and immune functioning, and will examine social-environmental influences on these systems. Special emphasis is placed on the social influences on stress-system biology, a major pathway by which socio-emotional experience affects the body, brain and developmental outcomes.

Emerging interactive technologies are rapidly transforming the ways in which we work, play, communicate, and learn. Research on tangible interaction attempts to blur the line between digital technologies and the broader physical, social, and cultural worlds within which computer use is situated. This course will explore the use of tangible interaction to create innovative learning experiences and will review both theoretical and technological foundations of the field. Topics may include distributed cognition, embodied interaction, cultural forms, and tangible interaction frameworks.

In this course we will examine the role of computational tools in the design of transformative teaching and learning environments. Grounded in perspectives from the Learning Sciences and Computer Science Education, we will cover topics of equity and inclusion, pedagogy, creative applications of computing, and the integration of computational ideas across multiple disciplines. We have cross-listed the course in Computer Science, Learning Sciences, and Teacher Education in order to foster cross-disciplinary dialogues that consider the affordances of computational ideas for designing learning environments that are asset-based and supportive of multiple routes to understanding various subject-matter. The course will include an optional practicum experience working in a K-12 classroom, informal learning space, or college-level classroom.