Computer Science Teachers, Postsecondary

Salary Overview

Job Outlook (2024–2034)

Key Skills

Knowledge Areas

What They Do

• Select and obtain materials and supplies, such as textbooks and laboratory equipment.
• Prepare course materials, such as syllabi, homework assignments, and handouts.
• Compile, administer, and grade examinations or assign this work to others.
• Prepare and deliver lectures to undergraduate or graduate students on topics such as programming, data structures, and software design.
• Maintain student attendance records, grades, and other required records.
• Keep abreast of developments in the field by reading current literature, talking with colleagues, and participating in professional conferences.
• Maintain regularly scheduled office hours to advise and assist students.
• Advise students on academic and vocational curricula and on career issues.

Tools & Technology

★ = Hot Technology (in-demand)

Education Requirements

Typical entry-level education: Master's Degree

Related Careers

Featured In

Careers with the highest skill compatibility from Computer Science Teachers, Postsecondary.

A Day in the Life

A computer science teacher's day varies significantly depending on whether they serve at a research university, teaching-focused college, or community college, but common threads unite the daily experience. Morning hours might involve delivering a lecture on database systems or leading a hands-on lab session where students work through programming assignments, debugging their code with instructor guidance and peer collaboration. Between classes, faculty hold office hours where students seek help understanding recursive algorithms, debugging their Java projects, or discussing career paths in technology. Research-active faculty dedicate significant blocks of time to their scholarly work: writing grant proposals, designing experiments, analyzing data, coding prototypes, and drafting papers for peer-reviewed conferences like ACM SIGCSE, IEEE, or AAAI. Midday often includes departmental meetings to discuss curriculum updates, new course proposals, student advising assignments, or accreditation requirements that ensure program quality standards are maintained. Afternoon work frequently involves grading student assignments, designing exam questions, updating course materials to reflect current industry practices, and mentoring graduate research assistants on their thesis projects. Faculty also invest time in service activities including serving on university committees, reviewing papers for academic journals, organizing student hackathons or programming competitions, and participating in outreach programs that introduce computing to underrepresented communities. The day may close with attending a research seminar, meeting with industry advisory board members about curriculum relevance, or preparing demonstration code and slides for the next day's lectures.

Work Environment

Computer science faculty work in academic environments that combine classroom teaching, research labs, office spaces, and increasingly, remote or hybrid settings for portions of their responsibilities. University campuses provide access to computing infrastructure, research labs with specialized hardware, libraries, and collaborative spaces where faculty interact with colleagues across disciplines. The academic calendar creates a distinctive work rhythm, with intense periods during the fall and spring semesters when teaching demands peak, followed by summer and winter breaks that allow focused research and curriculum development time. Workload expectations at research universities often exceed forty hours per week, with faculty balancing teaching, research, advising, committee service, and conference travel in a self-directed manner with considerable autonomy. Teaching-focused institutions provide lighter research expectations but higher course loads, typically four to five courses per semester at community colleges compared to two to three at research universities. The collegial academic culture values intellectual discourse, and faculty enjoy significant freedom to pursue research questions and pedagogical approaches that align with their interests and expertise. Job stability is exceptionally strong for tenured faculty, though the path to tenure is competitive and stressful, and the growing reliance on adjunct and non-tenure-track positions in higher education has created a bifurcated job market. Benefits typically include tuition reimbursement for dependent education, sabbatical leave for research every six to seven years, retirement contributions, and access to university facilities and events.

Career Path & Advancement

The path to becoming a computer science professor at a research university typically requires a PhD in computer science or a closely related field, involving four to seven years of doctoral study following a bachelor's or master's degree. Doctoral programs combine advanced coursework in a specialization area—such as machine learning, systems, theory, or human-computer interaction—with original research culminating in a dissertation that contributes new knowledge to the field. After completing the PhD, many aspiring professors complete one or two postdoctoral research positions to strengthen their publication record and build an independent research program before seeking tenure-track appointments. Community colleges and some teaching-focused institutions may hire faculty with master's degrees, providing a faster entry point that emphasizes pedagogical expertise over research output. The tenure process at research universities involves a rigorous six-to-seven-year evaluation of research productivity, teaching effectiveness, and professional service, culminating in a tenure decision that provides permanent employment security. Post-tenure advancement progresses through associate professor and full professor ranks, with distinguished or endowed chair positions representing the pinnacle of academic recognition. Some faculty pursue administrative leadership roles as department chair, associate dean, or dean of engineering and computing, while others maximize their research impact by directing large-scale research centers or labs.

Specializations

Computer science faculty specialize in research areas and teaching concentrations that reflect the discipline's remarkable breadth and depth. Artificial intelligence and machine learning specialists teach courses in neural networks, natural language processing, computer vision, and reinforcement learning while conducting research that pushes the state of the art in algorithmic intelligence. Systems and architecture faculty focus on operating systems, computer networks, distributed systems, and hardware-software co-design, training students in the foundations that underpin all computing infrastructure. Theory and algorithms specialists teach computational complexity, formal languages, graph theory, and algorithm design, establishing the mathematical foundations that prove what computers can and cannot efficiently accomplish. Software engineering faculty emphasize development methodologies, software design patterns, testing strategies, and project management, often incorporating industry best practices and team-based capstone projects. Cybersecurity educators teach network security, cryptography, digital forensics, and ethical hacking, preparing students for the rapidly growing security workforce while conducting research on emerging threats and defenses. Data science and database faculty cover statistical computing, data mining, big data systems, and information retrieval, bridging computer science with applied statistical methods. Human-computer interaction specialists research and teach user interface design, accessibility, usability testing, and emerging interaction modalities like virtual and augmented reality.

Pros & Cons

Industry Insight

Computer science education is undergoing significant transformation driven by enrollment surges, technology evolution, and changing expectations for what constitutes effective preparation for computing careers. Student demand for computer science programs has grown dramatically, with undergraduate enrollment tripling at many institutions over the past decade, creating capacity challenges that departments address through larger class sizes, online delivery, and innovative pedagogical approaches. The integration of AI and machine learning into the curriculum has become essential, with departments rapidly developing new courses and weaving AI concepts into existing courses across the curriculum from introductory programming to senior capstone projects. Active learning pedagogies, including flipped classrooms, pair programming, and project-based learning, have gained strong evidence-based support and are replacing traditional lecture-only formats at forward-thinking institutions. AI tutoring tools and automated grading systems are beginning to transform how faculty deliver instruction and assess student learning, enabling more personalized feedback at scale. The emphasis on diversity, equity, and inclusion in computer science has grown substantially, with departments implementing broadening participation initiatives, inclusive pedagogical practices, and curriculum changes aimed at making computing education welcoming and accessible to students from all backgrounds. Industry partnerships have deepened, with companies funding faculty positions, sponsoring student projects, providing guest lecturers, and influencing curriculum through advisory boards that ensure graduates possess current, relevant skills. The growth of online and hybrid delivery modes has expanded access to computer science education while raising questions about academic integrity, student engagement, and the optimal balance between in-person and virtual instruction.