Seven lessons for AI-robust assessment design in higher education
DOI:
https://doi.org/10.12697/eha.2026.14.2.02aKeywords:
assessment, artificial intelligence, higher education, assessment designAbstract
Tehisintellekti laialdane kasutuselevõtt üliõpilaste seas on tekitanud vajaduse tegeleda hindamissüsteemide ümberkujundamisega. Olukorras, kus 89% üliõpilastest kasutab õppetöös tehisintellekti ja ligi 80% teeb seda hindeliste tööde puhul, on õppeasutustel keeruline leida tasakaalu akadeemilise petturluse ennetamise ja üliõpilaste ettevalmistamise vahel tehisintellekti vastutustundlikuks kasutamiseks. Varasemad uuringud käsitlevad selle probleemi mõningaid aspekte, kuid ükski neist ei esita terviklikult praktilisi soovitusi, millele toetudes hindamissüsteeme kujundada. Artikkel on kirjutatud narratiivse ülevaatena ja tugineb hiljutistele teadusartiklitele, varasematele ülevaadetele ja hiljuti avaldatud uuringute tulemustele, mille tulemusena esitatakse kõrgharidusspetsialistidele praktilisi juhiseid ja soovitusi. Artikkel sisaldab seitset soovitust tehisintellektikindla hindamissüsteemi kujundamiseks: 1) akadeemilise petturluse ennetamine ei tohi toimuda hindamise valiidsuse arvelt; 2) arutelude tulemusena saadud lahendusi (nt tehisintellekti kasutamise eeskirjad) tuleb kombineerida hindamise struktuurse ümberkujundamisega; 3) soovitatav on kahesuunaline lähenemisviis, mis eristab iseseisvalt saadud tulemust tehisintellekti abil täiustatud tulemusest; 4) hindamise ülesehitus peaks tagama sisemise kognitiivse koormuse säilimise, võimaldades samal ajal kõrvalise koormuse vähendamist; 5) tehisintellekti väljundi kriitiliselt hindamise võimest on saanud keskne oskus, mis eeldab laialdasi erialateadmisi; 6) mitmekesine hindamismeetodite kogum (Šveitsi juustu mudel) vähendab haavatavust nii petturluse kui ka eelarvamusi valiidsuse suhtes ja 7) tehisintellektikindel hindamine nõuab õppejõududelt püsivat ja integreeritud professionaalset arengut. Need seitse soovitust pakuvad praktikas rakendatavat raamistikku õppeasutustele, kus soovitakse säilitada hindamise valiidsust, toetada õppimist ja tagada akadeemilist ausust keskkonnas, kus tehisintellekt on laialdaselt kasutuses.
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