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DTSTART;TZID=America/Denver:20251201T170000
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DESCRIPTION:The rapid evolution of AI and data-driven technologies presents
  both opportunities and challenges in education. In this research talk\, I
  will explore the synergy between AI and human elements in education. This
  bi-directional bridge involves using AI models enriched with human knowle
 dge for educational purposes while harnessing AI to enhance education and 
 knowledge. Dr. Yang Shi will begin by discussing how domain features in st
 udents' programming data can uncover common errors without labels through 
 clustering code embeddings. This technology offers a novel approach to err
 or discovery. Dr. Shi will then delve into deep models for knowledge traci
 ng tasks\, providing predictive support to students while offering interpr
 etability on code structures leading to specific predictions. These projec
 ts demonstrate how incorporating domain features enhances data-driven mode
 ls in education. Dr. Shi will then introduce the KC-Finder model\, which a
 utonomously uncovers students' practiced knowledge from programming logs\,
  thanks to the incorporation of knowledge component properties. This work 
 highlights the potential of merging educational theory with AI models for 
 education. Dr. Shi's research has focused on infusing human knowledge into
  AI models\, and in the future\, he plans to develop tools and methods to 
 further enhance human knowledge through improved AI models. Dr. Shi will c
 onclude by briefly outlining his future research plans in this direction\,
  offering a glimpse of the exciting possibilities ahead.\n\nSpeaker(s): Dr
 . Shi\, \n\nVirtual: https://events.vtools.ieee.org/m/512286
LOCATION:Virtual: https://events.vtools.ieee.org/m/512286
ORGANIZER:smehalingam@binghamton.edu
SEQUENCE:13
SUMMARY:Bridging Human and AI for Education
URL;VALUE=URI:https://events.vtools.ieee.org/m/512286
X-ALT-DESC:Description: <br /><div style="font-family: Aptos\,Aptos_Embedde
 dFont\,Aptos_MSFontService\,Calibri\,Helvetica\,sans-serif\; font-size: 12
 pt\; color: rgb(0\,0\,0)\;">The rapid evolution of AI and data-driven tech
 nologies presents both opportunities and challenges in education. In this 
 research talk\, I will explore the synergy between AI and human elements i
 n education. This bi-directional bridge involves using AI models enriched 
 with human knowledge for educational purposes while harnessing AI to enhan
 ce education and knowledge. Dr. Yang Shi will begin by discussing how doma
 in features in students' programming data can uncover common errors withou
 t labels through clustering code embeddings. This technology offers a nove
 l approach to error discovery. Dr. Shi will then delve into deep models fo
 r knowledge tracing tasks\, providing predictive support to students while
  offering interpretability on code structures leading to specific predicti
 ons. These projects demonstrate how incorporating domain features enhances
  data-driven models in education. Dr. Shi will then introduce the KC-Finde
 r model\, which autonomously uncovers students' practiced knowledge from p
 rogramming logs\, thanks to the incorporation of knowledge component prope
 rties. This work highlights the potential of merging educational theory wi
 th AI models for education. Dr. Shi's research has focused on infusing hum
 an knowledge into AI models\, and in the future\, he plans to develop tool
 s and methods to further enhance human knowledge through improved AI model
 s. Dr. Shi will conclude by briefly outlining his future research plans in
  this direction\, offering a glimpse of the exciting possibilities ahead.<
 /div>\n<div style="font-family: Aptos\,Aptos_EmbeddedFont\,Aptos_MSFontSer
 vice\,Calibri\,Helvetica\,sans-serif\; font-size: 12pt\; color: rgb(0\,0\,
 0)\;">&nbsp\;</div>
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