Our Top Three Reads on CS Education: Roles of the University, Math Requirements, and Research Experiences for Undergraduates

Many problems confront CS educators today: a rapidly evolving computing landscape, the ubiquity and accessibility of generative AI tools, complex ethical issues surrounding the use of artificial intelligence technologies, and the various challenges in promoting equity in computing education. In this "Advances in Computing" edition, we bring you three articles packed with meaningful insights and actionable recommendations. Check them out now!

1) Artificial Intelligence, Social Responsibility, and the Roles of the University

AI technologies deployed in industry today are far more powerful than the early AI technologies created in university laboratories. We ask: What roles can the university now play in the socially responsible development and use of AI technologies? While many industrial organizations and governments have published statements of principles for social responsibility with AI technologies, we go beyond statements of principles to recommendations for actions by universities, particularly those in the U.S.

Learn how universities can influence socially responsible use of AI technology development and use here.

2) An Analysis of the Math Requirements of 199 CS BS/BA Degrees at 158 U.S. Universities

There is no consensus across U.S. universities as to the placement of discrete math and calculus in the CS course sequence, and indeed early placement can serve as an institutional barrier which impacts students’ discovery, retention, and persistence in computing.

In this article, Carla E. Brodley , McKenna Quam , and Mark Weiss present their methodology, results, and guidelines published by ABET3 and the Association for Computing Machinery (ACM) and offer several recommendations for CS departments to consider. Read more here.

3) Research Experiences for Undergraduates Are Necessary for an Equitable Research Community

Suppose a student wants to apply for Ph.D. programs in programming language theory. It turns out their undergraduate institution does not do programming language theory. As a result they lack the research experience sometimes required for entry into Ph.D. programs. One option could be to apply for a master’s degree, which would both let them specialize in courses relevant to programming language theory while also building up research experience. This is a great option, except that many programs can be prohibitively expensive—often the first year salary of a new graduate. This is inequity.

For Joshua Sunshine and Joey Velez-Ginorio, research internships for undergraduates has a necessary part of any solution toward equity in computing education. In this article, they share valuable experiences on how to effectively start and run a Research Experiences for Undergraduates (REU) program. Learn more here.

Unlock our past editions:

Summer Reads on AI: Explainability, Intent-Based Networking, Co-Pilot, Copyright

Our Top Three Reads of June: the Trolley Problem, vulnerable LLMs, and Charles Babbage

Our Top Three Reads of May

Our Top Three Reads of April

Our Top Three Reads of March

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