AIED 2023-AI-ASSISTED MATH TUTORING: AIED 2023 Workshop: Towards the Future of AI-augmented Human Tutoring in Math Learning Tokyo, Japan, July 3-7, 2023 |
Conference website | https://sites.google.com/andrew.cmu.edu/aied2023workshop/home |
Submission link | https://easychair.org/conferences/?conf=aied2023aiassistedma |
Submission deadline | June 2, 2023 |
LOCATION & DATE
Date: July 3rd, 2023
Time: 9am-4pm (Japan Standard Time, GMT+9)
Location: WS-2 Room 101
Hitotsubashi Hall
National Center of Sciences Building 2F
Hitotsubashi UniversityTokyo, Japan
Link to AIED 2023: https://www.aied2023.org/
LEVERAGING AI AND HUMAN TUTORING
The primary challenge to improving middle school math achievement is providing all students equitable access to the existing high-quality learning opportunities that we know to be effective. Students from economically disadvantaged and historically underserved backgrounds can learn just as well as their peers when given the same opportunities, but they are more likely to experience learning gaps due to a lack of access to these learning opportunities [2]. High-dosage human tutoring can produce dramatic learning gains, particularly if tutors are well-trained in providing students social-motivational support [4]. However, low-income students lack access to well-trained tutors, evidenced by the 16 million low-income children on the waitlist for high-quality afterschool programs [1]. In addition, the estimated costs of $2500+ per student for individualized tutoring prohibits student access [3]. Human tutoring alone cannot meet present students need. Sustainable and scalable tutoring infrastructures are possible through the combined synergy of artificial intelligence (AI)-assisted and human technologies that can be achieved through novel and well-engineered AI-supported tutoring models.
AI-assisted tutoring shows promise and can potentially double learning outcomes [5], but analytics show that many students, especially from low-income backgrounds, are not getting sufficient learning opportunities. Student inaccessibility can be attributed to a variety of factors, including: not having sufficient access to the medium of using AI, such as digital devices and internet; issues facing inclusion with inadequate support of diverse student needs, such as English language learners and students with disabilities; and a lack of understanding of AI capabilities and limitations [7]. The challenges facing math learning related to access, equity, fairness, and inclusion have fostered collaborative and focused efforts on AI-assisted human-technology ecosystems that increase learning opportunities for all students.
This workshop aims to facilitate discussion and engagement among the Artificial Intelligence in Education (AIED) community regarding AI-assisted individualized learning tools to improve middle school teaching and tutoring. In particular, the workshop hosts updates on progress, findings, and challenges to AI-supported personalized instruction. We invite empirical and theoretical papers aligned with the theme particularly (but not exclusively) within the following areas of research and application:
THEME
- AI-assisted and Human Tutoring Systems: Insight into better understanding and supporting human, AI-assisted, and interactive learning technologies related to individualized instruction
- Delivery and Scale: Efficacy of different human tutoring delivery systems (e.g., video, audio, chat) and the corresponding needed differentiated support; Different models for scaling including peer tutoring, computer tutoring, etc.
- Training Development: Tutor and teacher training development that recognizes diverse experiences and backgrounds, in relation to AI-assisted tutoring support structures
- Equity and Inclusion: Issues facing equity and inclusion, with focus on intelligent techniques to support students from under resourced communities
- Ethics: Privacy and transparency of intelligent techniques, such as using federated machine learning and explainable AI to examine data ownership and human-AI collaboration; Transferability and fairness of predictive models across educational contexts.
- Evaluation: Program evaluation, such as applications using large-language models or dataset development for reinforcement learning of models; Methods of measuring student growth, with possible insights into dosage; Evidence of learning outcomes
- Key Challenges: Barriers, considerations, and challenges to providing human and AI-based tutoring and individualized instruction at scale
- Interoperability: How do AI and human tutoring systems interact with existing technological and social systems?
RELEVANCE & CALL FOR PAPERS
There is a concerted effort within the AIED community to increase learning opportunities among economically disadvantaged and historically underrepresented students. The COVID-19 pandemic had a severe impact on education globally. The U.S. has lost nearly twenty years of math progress among middle school students [6], with racial and economic learning gaps preventing millions of students from realizing their potential. By leveraging the power of AI, the AIED community is working to provide equitable learning opportunities and helping bridge the persistent opportunity gap in action.
The workshop will include presentations of accepted papers and facilitated discussion sessions. We will solicit papers relevant to the themes using the short or long-paper format described in the conference proceedings guidelines. Papers will go through a single-blind review process, with reviewers anonymous and authors known. Reviewers will be required to make a recommendation of either acceptance or rejection of the paper and explain their reasoning behind their decision. They will assess the paper based on three criteria, using a scoring system of -1, 0, or 1; alignment with the workshop’s theme, level of interest to AIED, and overall quality. Authors of accepted papers will provide presentations at the conference. Below is the call for papers schedule:
Call for papers: March 31, 2023
Paper submission deadline: May 26, 2023 June 2, 2023
Paper review period: May 27, 2023 - June 10, 2023 June 3, 2023 - June 10, 2023
Final paper decisions: June 11, 2023 - June 14, 2023
Notification of acceptance: June 15, 2023 June 20, 2023
Camera-ready deadline: June 19, 2023 June 26, 2023
Workshop day: July 3rd, 2023
TARGET AUDIENCE & PARTICIPATION
The target audience consists of researchers, educational practitioners, businesses, policymakers, and anyone among the AIED community interested in enriching their knowledge of AI-assisted and human tutoring working in synergy to provide individualized learning experiences. The workshop intends to set a wider agenda related to AI and human tutoring to grasp novel research ideas, interesting findings, and key insights to meet the practical needs of those involved in the research and development of AI-assisted and human tutoring ecosystems. This workshop is hybrid with no limit on the number of remote participants.
WORKSHOP FORMAT
We propose a full-day hybrid workshop with following activities: 1) presentations of accepted papers with Q&A, 2) small-group discussions on the conference themes, 3) reports of small-group discussions, 4) a moderated panel with audience participation focused on next steps, and 5) a closing summary and discussion. These activities will be oriented toward the workshop goals to develop shared understanding of the current state of AI-augmented human math tutoring and to pose key research questions and challenges for future research. The length of presentations will be determined by the organizing committee based on the maturity of the work, level of interest among AIED, and significance to the themes. Small group discussions will follow, and these will be aligned with the eight research areas described. A subsequent whole-group discussion will contain a question-and-answer period with in-person panelists. A summary of the key issues and responses from the panel discussion, along with commonalities among accepted papers, will be published in Volume 2 of the conference proceedings.
ORGANIZING COMMITTEE (alphabetical order)
- Vincent Aleven, Ph.D., Carnegie Mellon University, aleven@cs.cmu.edu
- Richard Baraniuk, Ph.D., OpenStax, Rice University, richb@rice.edu
- Emma Brunskill, Ph.D., Stanford University, ebrun@cs.stanford.edu
- Scott Crossley, Ph.D., Vanderbilt University, scott.crossley@vanderbilt.edu
- Dora Demszky, Ph.D., Stanford University, ddemszky@stanford.edu
- Stephen Fancsali, Ph.D., Carnegie Learning, sfancsali@carnegielearning.com
- Shivang Gupta, Carnegie Mellon University, shivangg@andrew.cmu.edu
- Kenneth Koedinger, Ph.D., Carnegie Mellon University, koedinger@cmu.edu
- Chris Piech, Ph.D., Stanford University, piech@cs.stanford.edu
- Steve Ritter, Ph.D., Carnegie Learning, sritter@carnegielearning.com
- Danielle R. Thomas, Ed.D., Carnegie Mellon University, drthomas@cmu.edu
- Simon Woodhead, Ph.D., Eedi, simon.woodhead@eedi.co.uk
- Wanli Xing, Ph.D., University of Florida, wanli.xing@coe.ufl.edu
REFERENCES
1. Afterschool Alliance. America After 3PM: Demand Grows, Opportunity Shrinks (2020).
2. Chine, D., Brentley, C., Thomas-Browne, C., Richey, J., Gul, A., Carvalho, P., Branstetter, L., Koedinger, K.: Educational equity through combined human-AI personalization: A propensity matching evaluation. In: International Conference on Artificial Intelligence in Education. pp. 366-377. Springer, Cham (2022).
3. Kraft, M., Falken, G.: A Blueprint for Scaling Tutoring Across Public Schools. (EdWorkingPaper: 20-335). Annenberg Institute at Brown University (2021).
4. Nickow, A., Oreopoulus, P., Quan, V.: The impressive effects of tutoring on prek-12 learning: A systematic review and meta-analysis of the experimental evidence. National Bureau of Economic Research (NBER), Working paper # 27476 (2020).
5. Pane, J., Griffin, B., McCaffrey, D., Karam, R.: Effectiveness of cognitive tutor algebra I at scale. In: Educational Evaluation and Policy Analysis, vol. 36, pp. 127-144 (2014).
6. U.S. Department of Education. Institute of Education Sciences, National Center for Education Statistics, National Assessment of Educational Progress (NAEP) (2022).
7. Vincent-Lancrin, S., van der Vlies, R.: Trustworthy artificial intelligence (AI) in education: Promises and challenges, OECD Education Working Papers, No. 218 (2020).
CONTACT
For questions about the AIED workshop, contact:
Danielle Thomas, EdD: drthomas@cmu.edu
Shivang Gupta: shivang@cmu.edu