Visual Representation of Co-Authorship with GPT-3: Studying Human-Machine Interaction for Effective Writing [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (16th, Bengaluru, India, Jul 11-14, 2023).

Autor/inn/en	Shibani, Antonette; Rajalakshmi, Ratnavel; Mattins, Faerie; Selvaraj, Srivarshan; Knight, Simon
Titel	Visual Representation of Co-Authorship with GPT-3: Studying Human-Machine Interaction for Effective Writing [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (16th, Bengaluru, India, Jul 11-14, 2023).
Quelle	(2023), (11 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Artificial Intelligence; Man Machine Systems; Writing (Composition); Collaborative Writing; Writing Processes; Visual Aids; Technology Uses in Education + Suchen Sie Ihr Suchwort? Künstliche Intelligenz; Mensch-Maschine-System; Schreibübung; Anschauungsmaterial; Technology enhanced learning; Technology aided learning; Technologieunterstütztes Lernen
Abstract	With the recent release of Chat-GPT by OpenAI, the automated text generation capabilities of GPT-3 are seen as transformative and potentially systemically disruptive for higher education. While the impact on teaching and learning practices is still unknown, it is apparent that alongside risks these tools offer the potential to augment human intelligence (intelligence augmentation, or IA). However, strategies for such IA, involving partnership of tool-human, will be needed to support learning. In the context of writing, an investigation of potential approaches is needed given empirical data and studies are currently limited. We introduce a novel visual representation CoAuthorViz to examine keystroke logs from a writing assistant where writers interacted with GPT-3 writing suggestions to co-write with the machine. We demonstrate the use of our visualization by exemplifying different kinds of writing behaviour from users writing with GPT-3 support and derive metrics such as their usage of GPT-3 suggestions in relation to overall writing quality indicators. We also release the materials open source to further progress our understanding of desirable user behaviour when working with such state-of-the-art AI tools. [For the complete proceedings, see ED630829.] (As Provided).
Anmerkungen	International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: https://educationaldatamining.org/conferences/
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01