Student chatbot use and perceptions in a course assignment: Comparing two geomatics courses
Bibliographic record
Abstract
Recent advancements in generative AI have significantly impacted higher education, especially through the integration of AI chatbots in course design, teaching, administration, and student support. However, empirical research on chatbot use in Geomatics education remains limited, particularly regarding students’ choice of platforms, preferences for multimodal prompts, perceived quality of chatbot responses, challenges encountered, and follow-up strategies. To address this gap, we investigated how students used chatbots as part of a given discussion assignment in two university-level Geomatics courses offered in Spring 2025: Measurement Science (n = 20) and Geospatial Analysis (n = 25). As part of the discussion assignment students were tasked to design five prompts based on lecture content, submit them to one of seven chatbot platforms, rate the responses, and provide written justifications. Student interactions were analyzed using regression models that accounted for repeated prompts per student. These models examined how platform choice, prompt modality, and other prompt characteristics influence response ratings and follow-up behavior. Findings revealed distinct patterns. For example, students in Geospatial Analysis posed proportionally more conceptual than computational questions; follow-up prompts were more likely when chatbots produced computational errors than when explanations were unsatisfactory; and data-enhanced prompts (e.g., with tabular inputs) received lower ratings than textual ones, underscoring unmet expectations for chatbot-based GIS data processing. The study concludes with a set of best practice recommendations for chatbot use based on these two courses, which may inform Geomatics instruction in similar university-level courses. • Students rated chatbot responses more favorably for surveying-related prompts than for GIS-related prompts • Multimodal prompts encouraged greater curiosity and exploration among students • Computation-focused prompts produced more unsatisfactory responses and follow-up questions than conceptual prompts • Students used a range of follow-up strategies when initial responses were unsatisfactory • Guidance is needed to help students develop advanced prompting techniques
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".