The WDSAI Faculty Excellence Exchange brings together higher education faculty and senior industry practitioners to address current problems in Data Science and AI. It is a convening platform of the World Data Science & AI Initiative, built on the premise that the quality of Data Science and AI education depends on how directly the classroom stays connected to the field students are preparing to enter.
The Exchange provides a continuing forum in which faculty and industry practitioners examine current problems together and consider their implications for curriculum, teaching, and assessment. Academic and industry perspectives inform one another in every session. The agenda for each cycle is shaped jointly by industry and academic co-leads, and sustained participation may lead to recognition through Fellowship.
Industry practice and academic curricula evolve on different cycles. New tools, workflows, and governance expectations can emerge between one formal program review and the next, and the same pattern appears across geographies and institution types. The gap is a recurring coordination challenge inherent to a
fast-moving discipline, and no individual educator or institution can close it alone.
The Exchange addresses that challenge from both directions. It gives faculty structured access to current practice, and it gives industry practitioners a direct view of how knowledge becomes curriculum, teaching, and assessment.
The Exchange is convened around the Faculty Competency Standards, jointly issued and maintained by the Data Science Council of America (DASCA) and the Artificial Intelligence Board of America (ARTiBA). The five dimensions of the framework provide the shared reference for session design and for the Implementation Notes participants prepare across the Exchange cycle.
The Exchange comprises four core session areas, together with special and regional sessions convened as problem briefs emerge and partner interest warrants. Each core session is delivered live online on two dates during each twelve-month cycle, scheduled to accommodate different time zones and academic calendars. Fellowship candidates complete one approved live session in each of the four core areas. Special and regional sessions do not satisfy a core-area requirement unless expressly designated in advance as counting toward it.
Each session is led by a senior industry practitioner and, at times, co-led by an experienced faculty member. The agenda for each cycle is developed from problem briefs that reflect current industry practice and academic priorities identified by participating faculty, with the final program shaped jointly by the industry and academic co-leads. Problem briefs are cleared for discussion and presented without confidential, proprietary, or personally identifiable information.
Every session is built around a live problem brief. Rather than presenting finished case studies, practitioners bring open questions from current practice. Faculty examine these questions alongside them, testing how curriculum, teaching methods, and assessment would need to respond.
Each cycle retains four core areas aligned with the Faculty Competency Standards. The session title, problem brief, and emphasis within each area are confirmed ahead of the cycle. The titles below are the planned expressions of those areas for the 2026–27 cycle.
Faculty Engagement & Leadership runs across the full cycle rather than appearing as a separate session. It is expressed through peer contribution during the Exchange and through the continuing involvement of Fellows, who may be invited to co-lead sessions, contribute problem briefs, or support future participants.
Following each session, participants prepare a short Implementation Note recording how the discussion relates to their curriculum, teaching, assessment, or wider academic work. The notes belong to the participant and together form the record used to confirm completion of the Fellowship requirements.
Sustained participation in the Exchange may lead to conferral as a Fellow of the World Data Science & AI Initiative (FWDSAI).
Candidates complete one session in each of the four core session areas within a twelve-month cycle and prepare a short Implementation Note following each session. Together, the notes record how the discussions relate to the participant's curriculum, teaching, assessment, or wider academic work. A joint academic-industry panel confirms completion of the Fellowship requirements before conferral.
Fellowship is conferred by WDSAI. The formal conferral document is co-signed by the Data Science Council of America (DASCA) and the Artificial Intelligence Board of America (ARTiBA) to attest that conferral occurred under the Faculty Competency Standards they jointly issue and maintain. Every Fellow is entered in the Register of Fellows maintained with the two councils and may use the post-nominal FWDSAI for life under the Fellowship Terms.
The path to Fellowship follows four stages.
Fellows remain part of the Exchange network and may be invited to co-lead sessions, contribute problem briefs, and support future participants.
Participation is open to faculty of recognized higher education institutions worldwide. Entry is by institutional nomination or by direct application with institutional endorsement.
Sessions for the 2026–27 cycle are announced as dates are confirmed. Each core session is offered twice during the cycle. The opening round is scheduled first, and a second date for each core area follows later in the cycle, announced as it is confirmed.
