Stu­dents at the UWI Arthur Lok Jack Glob­al School of Busi­ness are par­tic­i­pat­ing in a mul­ti-stage, AI-sup­port­ed busi­ness sim­u­la­tion de­signed to en­hance ap­plied de­ci­sion-mak­ing, col­lab­o­ra­tion, and re­flec­tive learn­ing with­in the school’s op­er­a­tions man­age­ment pro­gramme.

In a news re­lease, the UWI Arthur Lok Jack Glob­al School of Busi­ness said the sim­u­la­tion, ti­tled Cap­tains of In­dus­tries, places stu­dents in the role of se­nior ex­ec­u­tives and re­quires them to re­spond to evolv­ing op­er­a­tional sce­nar­ios de­signed to mir­ror re­al-world busi­ness pres­sures. Par­tic­i­pants must make strate­gic de­ci­sions, jus­ti­fy those de­ci­sions in struc­tured writ­ten re­spons­es, and as­sess their team­mates' judg­ments as the ex­er­cise pro­gress­es through suc­ces­sive stages.

Un­like tra­di­tion­al pro­grammed sim­u­la­tions, where out­comes shift on­ly if pre­set rules are rewrit­ten, this plat­form in­cor­po­rates agen­tic AI. That means sim­u­lat­ed ac­tors can as­sess sit­u­a­tions, se­lect ac­tions, and ad­just be­hav­iour as con­di­tions change, in­tro­duc­ing a lev­el of un­pre­dictabil­i­ty sim­i­lar to live op­er­at­ing en­vi­ron­ments. Pro­grammed sim­u­la­tions typ­i­cal­ly re­in­force stan­dards through fixed scripts and stored da­ta. By con­trast, the agen­tic mod­el is in­tend­ed to pro­mote both stan­dards and adapt­abil­i­ty.

Each stu­dent nav­i­gates the ex­er­cise through an on­line dash­board that tracks com­plet­ed stages, per­for­mance in­di­ca­tors, and gen­er­a­tive AI feed­back. The AI re­spons­es are based on trained man­age­ment guide­lines de­vel­oped by the sim­u­la­tion’s in­no­va­tor, Dr Kevin Fleary. At every stage, stu­dents con­front sce­nario-based chal­lenges in­volv­ing op­er­a­tional con­straints, trade-offs, and com­pet­i­tive pres­sures, re­quir­ing an­a­lyt­i­cal rea­son­ing rather than the rote ap­pli­ca­tion of the­o­ry in a same-day as­sess­ment set­ting.

Peer eval­u­a­tion forms a cen­tral com­po­nent of the de­sign. Team mem­bers re­view one an­oth­er’s de­ci­sions and ei­ther ap­prove or re­ject them. These eval­u­a­tions con­tribute to a per­for­mance met­ric that mea­sures how close­ly a stu­dent’s judg­ment aligns with over­all team trends. Fi­nal re­sults com­bine in­di­vid­ual per­for­mance da­ta with com­pa­ny av­er­ages, giv­ing both fac­ul­ty and par­tic­i­pants a de­tailed break­down at the end of the sim­u­la­tion.

The plat­form al­so pro­vides fac­ul­ty with ad­min­is­tra­tive over­sight tools, in­clud­ing co­hort-lev­el an­a­lyt­ics, growth track­ing, and ex­portable grade re­ports. Sim­u­la­tion da­ta can be archived to sup­port in­ter­nal qual­i­ty re­views, as­sur­ance-of-learn­ing process­es, and ac­cred­i­ta­tion re­quire­ments.

The ini­tia­tive is led by Dr Kevin Fleary, a fac­ul­ty mem­ber at the school, with tech­ni­cal sup­port from Key­on Thomas, an ad­junct fac­ul­ty mem­ber and in­for­ma­tion tech­nol­o­gy busi­ness pro­fes­sion­al spe­cial­is­ing in AI ar­chi­tec­ture.

The ex­er­cise re­flects the in­sti­tu­tion’s broad­er push to­ward tech­nol­o­gy-en­abled learn­ing and ex­pe­ri­en­tial as­sess­ment, with a fo­cus on prepar­ing MBA grad­u­ates to man­age com­plex, da­ta-dri­ven de­ci­sions in un­cer­tain op­er­at­ing en­vi­ron­ments.