A Project-based Learning Activity Involving Hybrid Intelligence in the Undergraduate Engineering Class
Abstract
Artificial intelligence (AI) is increasingly embedded in academic practice, creating opportunities to support learning and improve educational outcomes. We implemented a project-based learning activity in an undergraduate engineering course in which students used meteorological data to construct a temperature time series and assess local climate change at a selected location from the national weather station network. They transformed raw datasets, organized files in spreadsheets, and automated routine calculations by prompting a large language model (LLM) to generate executable code for a collaborative programming platform. As a structured form of human–AI collaboration, the activity contributes to discussions of hybrid intelligence in education by allowing students to focus on higher-level cognitive tasks.
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BibTeX
@misc{fleischfresser2026projectbased,
title = {{A Project-based Learning Activity Involving Hybrid Intelligence in the Undergraduate Engineering Class}},
author = {Luciano Fleischfresser},
howpublished = {EarthArXiv},
year = {2026},
month = sep,
doi = {10.31223/x5n794},
url = {https://eartharxiv.org/repository/view/14740/},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - A Project-based Learning Activity Involving Hybrid Intelligence in the Undergraduate Engineering Class
AU - Luciano Fleischfresser
PY - 2026
DA - 2026/09/02
PB - EarthArXiv
DO - 10.31223/x5n794
UR - https://eartharxiv.org/repository/view/14740/
AB - Artificial intelligence (AI) is increasingly embedded in academic practice, creating opportunities to support learning and improve educational outcomes. We implemented a project-based learning activity in an undergraduate engineering course in which students used meteorological data to construct a temperature time series and assess local climate change at a selected location from the national weather station network. They transformed raw datasets, organized files in spreadsheets, and automated routine calculations by prompting a large language model (LLM) to generate executable code for a collaborative programming platform. As a structured form of human–AI collaboration, the activity contributes to discussions of hybrid intelligence in education by allowing students to focus on higher-level cognitive tasks.
ER -