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Computer science explains how to build computational systems; behavioral science examines how people act and make decisions; and AI puts computational systems into tasks where human judgment and machine output can meet. Combining the three offers a useful way to think about technology not just as code, but as something people use, interpret, and rely on.
What is known about the essay behind this title?
DEV Community search results attribute an essay titled “Why I’m Combining Computer Science, Behavioral Science, and AI” to Levi Protas. The result labels it a two-minute read and dates it September 19, but does not specify the year. Protas’s DEV Community profile describes him as a computer science student at Oregon State University with a background in healthcare and behavioral science, and lists interests including Python, cybersecurity, AI, software development, and practical automation.
The full essay was not available to verify. Its particular motivations, examples, and conclusions therefore cannot be attributed to Protas with confidence. The framework below explains why these fields can complement one another; it should not be read as a summary of the essay’s unverified argument.
What does each field contribute?
| Field | Central question | What it helps you examine |
|---|---|---|
| Computer science | How can a computational system be designed and built? | Algorithms, software, data, and the system’s behavior under specified conditions. |
| Behavioral science | How do people act, make decisions, and respond to their circumstances? | People’s goals, choices, habits, and the context in which they use technology. |
| Artificial intelligence | How can computational methods perform tasks that involve prediction, generation, or decision support? | System outputs and the ways those outputs enter human activities and decisions. |
These are complementary lenses, not interchangeable disciplines. A technically correct system can still be confusing or poorly suited to a user’s task. Conversely, understanding a human need does not by itself explain whether a system can meet it reliably.
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Why does behavioral science matter when building AI?
AI outputs often arrive as advice, predictions, or generated material that someone must assess. That makes the human response part of the practical outcome: users may accept an output, question it, ignore it, or intervene. A 2026 analytical review of human-computer interaction research treats human reliance on AI advice—and the design of appropriate reliance and intervention—as a research problem. It supports asking how people use AI advice, not assuming that AI necessarily improves decisions.
Behavioral science can help frame questions about the person and situation around a system: What is the user trying to do? What information do they have? How might they interpret an output? When might they trust it or decide to check it? These questions can guide design and evaluation alongside technical questions about how the system produces its output.
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Why study technology in context?
A system does not operate in isolation from the activity it is meant to support. A 2009 dissertation on mobile-phone use offers a historical, conceptual example: it argues for examining how, what, and why people do things with technology. Although it predates current AI systems and is not evidence about their effectiveness, the idea is useful when deciding what to observe. Looking at a tool only as a device or codebase can miss the purpose and circumstances of its use.
For an AI-supported task, that means evaluating more than whether the software returns an output. It also means considering what the person is doing, how the output fits that activity, and what role the person has in deciding what happens next.
Where do the disciplines meet in practice?
- Define the task: Use behavioral questions to understand the person’s goal and the setting; use computer-science knowledge to establish what a system can technically do.
- Design the interaction: Consider how the output is presented and what options users have to inspect, question, or act on it.
- Evaluate the whole decision context: Examine both system performance and how people respond to its output. An accurate output alone does not establish that people will use it appropriately.
- Revise based on evidence: Treat user behavior and system behavior as distinct things to investigate, rather than assuming one explains the other.
What this combination does—and does not—establish
Studying computer science, behavioral science, and AI can provide a broader vocabulary for analyzing human-facing technology: how it works, how people behave around it, and how system outputs affect decisions. That is a rationale for connecting the fields, not proof that one educational combination guarantees better AI or a particular career outcome. The available information about Protas’s essay does not establish his personal anecdotes, specific conclusions, or outcomes.
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