BEGIN:VCALENDAR
PRODID:-//github.com/ical-org/ical.net//NONSGML ical.net 4.0//EN
VERSION:2.0
BEGIN:VTIMEZONE
TZID:America/New_York
X-LIC-LOCATION:America/New_York
BEGIN:STANDARD
DTSTART:20241103T020000
RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11
TZNAME:EST
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
END:STANDARD
BEGIN:DAYLIGHT
DTSTART:20250309T020000
RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3
TZNAME:EDT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
END:DAYLIGHT
END:VTIMEZONE
BEGIN:VEVENT
DESCRIPTION:AI tools can now write policy memos\, draft research papers\, 
 and complete many of the assignments we rely on to evaluate student learn
 ing. As these tools become more capable and more embedded in professional
  practice\, the question is no longer whether students are using AI. The 
 question is whether our assessments are designed to reveal learning that 
 AI cannot fake. This journal club session takes a forward-looking approac
 h to assessment design in an AI-saturated environment. Drawing on literat
 ure in learning science\, cognitive psychology\, and educational measurem
 ent\, we'll build a shared framework for designing assessments that requi
 re and make visible genuine disciplinary thinking\, even when AI is in th
 e room. Participants are asked to read one paper provided in advance. The
  session will be discussion-driven\, with a focus on applying new framewo
 rks to your own courses. The article I would like you to read in advance 
 of this journal club is a Substack article by Becky Allen. " The Reality 
 of Understanding: Why learning cannot be seen directly\, but can still be
  known " https:/ /profbeckyallen.substack.com/p/the-reality-of-understand
 ing I will provide additional references during the journal club\, but th
 is essay does a really nice job of starting to recalibrate our assumption
 s about assessment in the face of the chaos being inflicted by AI. "The p
 roblem with educational measurement is not that learning is invisible. It
  is that many different invisible things can cast the same visible shadow
 . Good assessment is the attempt to make those shadows diverge. " No prio
 r background in learning science or AI required. All instructors welcome.
 \n\nAdmission:\nFree: YSPH Faculty\n\nDetails URL:\nhttps://medicine.yale
 .edu/event/oaa-journal-club-what-ai-reveals-about-how-we-measure-learning
 /\n
DTEND;TZID=America/New_York:20261015T130000
DTSTAMP:20261003T224018Z
DTSTART;TZID=America/New_York:20261015T120000
GEO:41.303666;-72.932218
LOCATION:Yale School of Public Health (LEPH)\, Conference Room 424\, 60 Co
 llege Street\, New Haven\, CT\, United States
SEQUENCE:0
STATUS:Confirmed
SUMMARY:OAA Journal Club: What AI Reveals About How We Measure Learning
UID:69517464-1c87-40cd-a31f-9074864680cb
END:VEVENT
END:VCALENDAR