AI Mentors for Student Projects: Spotting Early Issues in Computer Science Proposals
Clicks: 33
ID: 282487
2025
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
Emerging Content
9.6
/100
33 views
17 readers
AI Quality Assessment
Not analyzed
Readership in this journal
EmergingRanked #673 of 803 articles by views in arXiv
Most read
Least read
Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 803 in total.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
When executed well, project-based learning (PBL) engages students' intrinsic
motivation, encourages students to learn far beyond a course's limited
curriculum, and prepares students to think critically and maturely about the
skills and tools at their disposal. However, educators experience mixed results
when using PBL in their classrooms: some students thrive with minimal guidance
and others flounder. Early evaluation of project proposals could help educators
determine which students need more support, yet evaluating project proposals
and student aptitude is time-consuming and difficult to scale. In this work, we
design, implement, and conduct an initial user study (n = 36) for a software
system that collects project proposals and aptitude information to support
educators in determining whether a student is ready to engage with PBL. We find
that (1) users perceived the system as helpful for writing project proposals
and identifying tools and technologies to learn more about, (2) educator
ratings indicate that users with less technical experience in the project topic
tend to write lower-quality project proposals, and (3) GPT-4o's ratings show
agreement with educator ratings. While the prospect of using LLMs to rate the
quality of students' project proposals is promising, its long-term
effectiveness strongly hinges on future efforts at characterizing indicators
that reliably predict students' success and motivation to learn.
| Reference Key |
lipton2025ai
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | Gati Aher; Robin Schmucker; Tom Mitchell; Zachary C. Lipton |
| Journal | arXiv |
| Year | 2025 |
| DOI |
DOI not found
|
| URL | |
| Keywords |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.