Equity at the forefront: A systematic research and development process of chatbot curriculum for massive open online courses

This paper describes our equity-driven development of a chatbot curriculum for a journalism professional development center’s massive open online courses (MOOCs). A curriculum refers to entire student experiences that occur during the whole learning process in this context. We used a sequential and iterative development process with four phases to research and develop the chatbot curriculum: experimentation, evaluation, research and development reiteration, and implementation and evaluation in an ongoing course. In Phase 1, we tested whether a chatbot interface designed to answer frequently asked questions (FAQs) could provide more equitable student support than traditional FAQ pages, considering the widespread student frustration about FAQ pages. A total of 120 diverse students participated in this phase. In Phase 2, we conducted a case study to contrast the experiences of 27 non-native English users who used the FAQ chatbot, identifying what distinguished their experiences from those of 15 native English user counterparts. Based on this phase’s findings, the chatbot was further trained using an enhanced training set to better support non-native English users. In Phase 3, using a topic analysis result of 3,645 forum discussion posts from the center’s previous 21 courses, we ensured the chatbot’s expertise to cover nine additional topic themes. Phase 4 is where we examine students’ experiences with the chatbot’s latest version as a built-in course tool in MOOCs. This study suggests that systematic research and development of a chatbot curriculum is indispensable to fostering equitable learning environments in MOOCs.

Publication Details

Journal
SHILAP Revista de lepidopterología
Published
2024-10-01
DOI
https://doi.org/10.30191/ets.202410_27(4).sp08
Citations
1
Primary Topic
AI in Service Interactions
Type
article
Field-Weighted Citation Impact
0.35
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Equity at the forefront: A systematic research and development process of chatbot curriculum for massive open online courses

1 citations
SHILAP Revista de lepidopterología
AI in Service Interactions
0.35
article

Equity at the forefront: A systematic research and development process of chatbot curriculum for massive open online courses

article en
1 citations

Abstract

This paper describes our equity-driven development of a chatbot curriculum for a journalism professional development center’s massive open online courses (MOOCs). A curriculum refers to entire student experiences that occur during the whole learning process in this context. We used a sequential and iterative development process with four phases to research and develop the chatbot curriculum: experimentation, evaluation, research and development reiteration, and implementation and evaluation in an ongoing course. In Phase 1, we tested whether a chatbot interface designed to answer frequently asked questions (FAQs) could provide more equitable student support than traditional FAQ pages, considering the widespread student frustration about FAQ pages. A total of 120 diverse students participated in this phase. In Phase 2, we conducted a case study to contrast the experiences of 27 non-native English users who used the FAQ chatbot, identifying what distinguished their experiences from those of 15 native English user counterparts. Based on this phase’s findings, the chatbot was further trained using an enhanced training set to better support non-native English users. In Phase 3, using a topic analysis result of 3,645 forum discussion posts from the center’s previous 21 courses, we ensured the chatbot’s expertise to cover nine additional topic themes. Phase 4 is where we examine students’ experiences with the chatbot’s latest version as a built-in course tool in MOOCs. This study suggests that systematic research and development of a chatbot curriculum is indispensable to fostering equitable learning environments in MOOCs.

SHILAP Revista de lepidopterología
Quality Education
Openalex Percentile: Top 28%
AI in Service Interactions
0.35
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.