TY - GEN
T1 - Potential Pitfalls of False Positives
AU - Dey, Indrani
AU - Gnesdilow, Dana
AU - Passonneau, Rebecca
AU - Puntambekar, Sadhana
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - Automated writing evaluation (AWE) systems automatically assess and provide students with feedback on their writing. Despite learning benefits, students may not effectively interpret and utilize AI-generated feedback, thereby not maximizing their learning outcomes. A closely related issue is the accuracy of the systems, that students may not understand, are not perfect. Our study investigates whether students differentially addressed false positive and false negative AI-generated feedback errors on their science essays. We found that students addressed nearly all the false negative feedback; however, they addressed less than one-fourth of the false positive feedback. The odds of addressing a false positive feedback was 99% lower than addressing a false negative feedback, representing significant missed opportunities for revision and learning. We discuss the implications of these findings in the context of students’ learning.
AB - Automated writing evaluation (AWE) systems automatically assess and provide students with feedback on their writing. Despite learning benefits, students may not effectively interpret and utilize AI-generated feedback, thereby not maximizing their learning outcomes. A closely related issue is the accuracy of the systems, that students may not understand, are not perfect. Our study investigates whether students differentially addressed false positive and false negative AI-generated feedback errors on their science essays. We found that students addressed nearly all the false negative feedback; however, they addressed less than one-fourth of the false positive feedback. The odds of addressing a false positive feedback was 99% lower than addressing a false negative feedback, representing significant missed opportunities for revision and learning. We discuss the implications of these findings in the context of students’ learning.
UR - http://www.scopus.com/inward/record.url?scp=85200266100&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85200266100&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-64315-6_45
DO - 10.1007/978-3-031-64315-6_45
M3 - Conference contribution
AN - SCOPUS:85200266100
SN - 9783031643149
T3 - Communications in Computer and Information Science
SP - 469
EP - 476
BT - Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky - 25th International Conference, AIED 2024, Proceedings
A2 - Olney, Andrew M.
A2 - Chounta, Irene-Angelica
A2 - Liu, Zitao
A2 - Santos, Olga C.
A2 - Bittencourt, Ig Ibert
PB - Springer Science and Business Media Deutschland GmbH
T2 - 25th International Conference on Artificial Intelligence in Education, AIED 2024
Y2 - 8 July 2024 through 12 July 2024
ER -