This report summarizes findings from the first phase of CRA’s LEVEL UP AI project, which convened 32 virtual roundtables with more than 200 experts to address the growing demand for undergraduate AI education. The discussions explored four focus areas: AI knowledge areas and pedagogy, infrastructure challenges, strategies to increase capacity, and ensuring AI education for all students. Key themes include the urgent need for faculty development, access to computing resources, and curriculum models that balance technical depth with ethical and societal considerations. The report also highlights institution-specific perspectives — from R1 universities to community colleges — and provides a curated list of AI education resources. Insights from these roundtables will inform the upcoming LEVEL UP AI Workshops, as well as NAIRR Pilot Conferences and Research Collaboration Network activities aimed at building a national AI education community.

September 2025

This report provides recommendations for evaluating computing researchers across academia, industry, government, and non-profit labs. It addresses evolving norms in research, teaching, mentoring, and service, and offers practical guidance for committees and administrators developing hiring, promotion, and tenure packages. The document emphasizes assessing the quality and impact of contributions — rather than volume of output — and highlights the importance of recognizing interdisciplinary work, industry collaborations, and diverse institutional contexts. It also includes guidance for external evaluations and support for junior scholars navigating career advancement.

September 2025

This document offers best practices for evaluating computing researchers, emphasizing the need to recognize the unique research culture of the field. It highlights the value of conference publications, diverse research outputs, interdisciplinary work, and the collaborative ecosystem spanning academia, industry, and government. It also stresses that evaluation practices should account for differences across computing subfields in publication norms, authorship practices, and resource environments.

July 2025

The Computing Research Association (CRA) and Computing Community Consortium (CCC) offer actionable guidance to organizational leaders seeking to advance interdisciplinary computing research. Based on insights from six expert roundtable discussions, this document outlines strategies for enabling collaboration, reducing proposal barriers, and supporting diverse, long-term interdisciplinary efforts. It encourages the development of tailored funding mechanisms, inclusive review processes, and training programs to foster innovation across disciplines.

June 2025

The Computing Research Association (CRA) and Computing Community Consortium (CCC) present a comprehensive guide to help researchers navigate interdisciplinary computing projects. This document offers strategies for finding collaborators, building trust, and setting clear research goals. It emphasizes the importance of transparent communication, flexible dissemination methods, and supporting career trajectories in interdisciplinary contexts. By following these best practices, researchers can enhance collaboration effectiveness and contribute to innovative computing research.

July 2024

The Computing Research Association (CRA) and Computing Community Consortium (CCC) offer actionable guidance to funders seeking to advance interdisciplinary computing research. Based on insights from six expert roundtable discussions, this document outlines strategies for enabling collaboration, reducing proposal barriers, and supporting diverse, long-term interdisciplinary efforts. It encourages the development of tailored funding mechanisms, inclusive review processes, and training programs to foster innovation across disciplines.

March 2025

This best practices report outlines effective strategies for building impactful partnerships with Minority Serving Institutions (MSIs). It provides guidance on engaging with MSI faculty, including understanding their unique responsibilities and developing long-term relationships. It covers best practices for research collaborations, student recruitment, and capacity-building initiatives to support the growth of MSI programs. Additionally, the report offers insights into planning successful institutional visits, maintaining ongoing relationships, and implementing effective retention strategies for minority graduate students. This resource offers actionable guidelines for fostering successful collaborations in computing research.

July 2024

This best practices report provides comprehensive guidelines for organizing computing research conferences that uphold high standards of responsibility and ethics. It covers key areas such as integrating harm prevention guidelines, responsible disclosure of vulnerabilities, the importance of ethics board reviews, and ensuring proper consent documentation. It also addresses accurate reporting and reproducibility practices, managing financial conflicts of interest, the use of generative AI, ethics in publication review, and establishing clear reporting and retraction procedures. This resource is designed to help foster a responsible and transparent research environment.

July 2024

The rising demand for computing degree programs has led to a rapid increase in teaching faculty positions in computing related fields. This white paper discusses how the diversity in teaching faculty jobs and hiring practices creates challenges for both candidates and departments and then recommends best practices for the hiring process.

April 2024

To achieve their educational mission, computing departments at research universities increasingly depend on full-time teaching faculty who choose teaching as a long-term career. This memo discusses the need for teaching faculty, explores the impact of teaching faculty, and recommends best practices. The best practices outlined here are designed to benefit everyone: teaching faculty, other faculty, students, and administrators.  These best practices recognize the unique and complementary role that full-time teaching faculty play in the educational mission of the academic computing research community.

August 2018

A careful distinction between quality and quantity is key to promoting the future growth of the computing and information field. Toward that end, this document advocates adjustments to hiring, promotion, and tenure practices as well as to the publication culture. Contributions in a small number of high quality publications or artifacts are what should be emphasized; success as a researcher is then not primarily a matter of numbers.

The recommendations that follow were developed over an 18-month period by the CRA Committee on Best Practices for Hiring, Promotion, and Scholarship. As part of this work, the committee conducted interviews in autumn 2013 with more than 75 academic and industry computing and information unit heads to understand the issues and gain insights from practice. Preliminary recommendations were vetted with department chairs and CRA Deans at the CRA Conference at Snowbird in July 2014.

February 2015

The Computing Research Association’s (CRA) Board of Directors has approved a Best Practices Guide, providing guidance to graduate students, postdocs, advisors and mentors, and departments and institutions on how to have a positive postdoctoral experience within computer science and engineering.  We encourage our colleagues throughout the community to take a look at the document — the latest in a series of white papers about the recent increase in postdocs in the field — and adopt these Best Practices.

December 2012

The fields of computing and information science and engineering have a strong commitment to interdisciplinary work, with CISE researchers collaborating with electrical engineers in the design of low-power chips; with linguists in the development of natural-language processing systems; with biologists in the exploration of the genetic code; with economists in the formation of theories of on-line commerce; and with statisticians in the discovery of new ways to extract information from rich sets of data—to name just a few examples.

July 2008

Universities and businesses have considerable incentive to cooperate in the development of intellectual property (IP). Businesses recognize universities for their rich talent pool and enthusiastic graduate students, while universities recognize businesses as a source of real-world problems, technical know-how, and funding. There are numerous examples of successful research collaborations in computer science, computer engineering, and electrical engineering. Mindful that some IP such as gene splicing and human growth hormone have produced “IP goldmines,” many university administrators (and some students and faculty) are eager to establish strong safeguards to protect their rights to intellectual property.

September 2003

The relentless pressure to innovate in the information technology (IT) industry has drawn university researchers and graduate students into entrepreneurial situations to an increasing degree. The trend affects the academic enterprise in diverse ways, both favorable and unfavorable. The risks and rewards are outlined, and the concept of a Commercialization Oversight Committee is described as a mechanism that can facilitate the best outcomes when interests conflict.

July 2001

The evaluation of computer science and engineering faculty for promotion and tenure has generally followed the dictate “publish or perish,” where “publish” has had its standard academic meaning of “publish in archival journals” [Academic Careers, 94]. Relying on journal publications as the sole demonstration of scholarly achievement, especially counting such publications to determine whether they exceed a prescribed threshold, ignores significant evidence of accomplishment in computer science and engineering. For example, conference publication is preferred in the field, and computational artifacts – software, chips, etc. – are a tangible means of conveying ideas and insight. Obligating faculty to be evaluated by this traditional standard handicaps their careers, and indirectly harms the field. This document describes appropriate evidence of academic achievement in computer science and engineering.

August 1999