BEGIN:VCALENDAR
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DESCRIPTION:Charlotte: tech\, today (Fri Sep 18\, America/New_York). Rolli
 ng: the window moves forward every day\, so subscribe rather than download
 . Discovered and maintained by CalDiscovery (https://caldiscovery.com). Ev
 ent details belong to their publishers. Contains information from CalDisco
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SOURCE:https://caldiscovery.com/charlotte-nc/today-tech.ics
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X-WR-CALDESC:Charlotte: tech\, today (Fri Sep 18\, America/New_York). Roll
 ing: the window moves forward every day\, so subscribe rather than downloa
 d. Discovered and maintained by CalDiscovery (https://caldiscovery.com). E
 vent details belong to their publishers. Contains information from CalDisc
 overy\, which is made available under the ODbL 1.0.
X-WR-CALNAME:Charlotte · tech · today · CalDiscovery
BEGIN:VEVENT
SUMMARY:AI²: Investigating student use of artificial intelligence and fac
 ulty-student perspectives on academic integrity
DTSTART:20260918T140000Z
DTEND:20260918T150000Z
DTSTAMP:20260914T182620Z
UID:tag:localist.com\,2008:EventInstance_53869363824691
CATEGORIES:tech
DESCRIPTION:Candidate Name: Terry Shirley\nProgram: Educational Leadership
  - Learning\, Design\, and Technology\nCommittee Chair: Dr. Ji Yae Bong​
 ​​​​​​\nCommittee Members: Dr. Beth Oyarzun\, Dr. Stella Kim\,
  Dr. Casey Davenport\nAbstract:\n\nArtificial intelligence has rapidly adv
 anced in recent years and now impacts many aspects of higher education. Ge
 nerative artificial intelligence (GenAI) offers significant opportunities 
 for student learning while raising complex questions about academic integr
 ity. A growing body of empirical research is emerging on how students use 
 GenAI\, with more limited work on what motivates that use and how students
  and faculty evaluate various GenAI assisted practices. Using a quantitati
 ve correlational and comparative design\, this study examined data collect
 ed from students and faculty at a large public university. GenAI adoption 
 was widespread: 92% of students reported using GenAI\, more than 80% had u
 sed it for academic purposes\, and 90% identified ChatGPT as the most comm
 only used GenAI tool. Adoption rates were generally consistent across demo
 graphic groups\, but differences emerged between STEM and non-STEM student
 s regarding specific uses. Regression analyses supported expectancy-value 
 theory (EVT) as a framework for understanding GenAI adoption. Utility and 
 attainment were the strongest predictors of academic GenAI use\, with the 
 model explaining 31.8% of the variance. EVT motivational constructs and pe
 rceived cost also significantly predicted students’ dishonesty evaluatio
 ns of a variety of GenAI use scenarios. Faculty generally rated unacknowle
 dged GenAI use as more dishonest than students did\, although both groups 
 shared a near identical hierarchy of ethical severity across uses. Acknowl
 edging GenAI use significantly reduced perceived dishonesty across all sce
 narios for both groups and substantially narrowed faculty/student percepti
 on gaps. These findings underscore the importance of transparent acknowled
 gment standards and clearly defined institutional guidance for responsible
  academic GenAI use.
LAST-MODIFIED:20260914T182620Z
LOCATION:McEniry\, 401
URL:https://campusevents.charlotte.edu/event/ai-investigating-student-use-
 of-artificial-intelligence-and-faculty-student-perspectives-on-academic-in
 tegrity?utm_source=caldiscovery&utm_medium=ics&utm_campaign=charlotte-nc
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END:VEVENT
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