{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/135740"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/135740","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Understanding the Applications of AI for Autistic STEM Student Mentorship","abstract":"Autistic college students in STEM face significant challenges in employment, such as low-pay and hiring discrimination compared to their neurotypical peers, despite existing mentorship programs for career development. This study investigates how artificial intelligence (AI) can be leveraged to enhance STEM mentorship for autistic students. Specifically, it examines students' acceptance of AI as a complementary support tool alongside human mentors, their previous mentorship experiences, their experiences and perceptions of AI, as well as their deeper visions for future AI integration in mentorship. Chapter 2 presents findings from an exploratory online survey of 203 autistic students, offering insights into their perceptions of AI-augmented mentorship, including perceived benefits, limitations, and areas of concern. Building on these results, Chapter 3 describes a follow-up interview study with 15 participants that explores their current academic and professional experiences typically supported by mentorship and their vision for how AI could support key mentorship activities. Thematic analysis reveals a strong desire for integrated, intelligent tools to facilitate online communication, time management, and emotional well-being, alongside valid concerns regarding the limitations and potential harms of current AI systems. The findings inform design considerations for future AI-supported mentorship and underscore the importance of accessibility and inclusion in the development of such systems. We conclude with recommendations for the design of future mentorship programs that effectively integrate AI to support the unique needs of autistic students in STEM. Further this work discusses the importance of universal, user-centered design for technology.","abstract_html":"Autistic college students in STEM face significant challenges in employment, such as low-pay and hiring discrimination compared to their neurotypical peers, despite existing mentorship programs for career development. This study investigates how artificial intelligence (AI) can be leveraged to enhance STEM mentorship for autistic students. Specifically, it examines students' acceptance of AI as a complementary support tool alongside human mentors, their previous mentorship experiences, their experiences and perceptions of AI, as well as their deeper visions for future AI integration in mentorship. Chapter 2 presents findings from an exploratory online survey of 203 autistic students, offering insights into their perceptions of AI-augmented mentorship, including perceived benefits, limitations, and areas of concern. Building on these results, Chapter 3 describes a follow-up interview study with 15 participants that explores their current academic and professional experiences typically supported by mentorship and their vision for how AI could support key mentorship activities. Thematic analysis reveals a strong desire for integrated, intelligent tools to facilitate online communication, time management, and emotional well-being, alongside valid concerns regarding the limitations and potential harms of current AI systems. The findings inform design considerations for future AI-supported mentorship and underscore the importance of accessibility and inclusion in the development of such systems. We conclude with recommendations for the design of future mentorship programs that effectively integrate AI to support the unique needs of autistic students in STEM. Further this work discusses the importance of universal, user-centered design for technology.","abstract_has_math":false,"creators":["Stil, Sophia"],"institution":"Virginia Tech","degree_name":"Master of Science","degree_level":"masters","degree_discipline":"Computer Science & Applications","degree_department":"Computer Science and#38; Applications","school":null,"contributors":[],"advisors":[],"committee_chairs":["Rho, Eugenia Ha Rim"],"committee_members":["Lee, Sang Won","Jeon, Myounghoon"],"year":2025,"date_issued":"2025-06-27","date_published":"2025-06-27","updated_at":"2026-07-24T05:56:30Z","subjects":["human-centered computing","accessibility"],"languages":["en"],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:44234"],"render_values":[{"text":"vt_gsexam:44234","href":null,"code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/10919/135740","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Rho, Eugenia Ha Rim"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Lee, Sang Won","Jeon, Myounghoon"]},{"key":"dc:contributor.department","label":"Department","values":["Computer Science and#38; Applications"]},{"key":"dc:creator","label":"Author","values":["Stil, Sophia"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-06-28T08:01:37Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-06-28T08:01:37Z"]},{"key":"dc:date.issued","label":"Date","values":["2025-06-27"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science & Applications"]},{"key":"thesis:degree_level","label":"Degree Level","values":["masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["human-centered computing","accessibility"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["In Copyright"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:44234"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10919/135740"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Autistic college students in STEM face significant challenges in employment, such as low-pay and hiring discrimination compared to their neurotypical peers, despite existing mentorship programs for career development. This study investigates how artificial intelligence (AI) can be leveraged to enhance STEM mentorship for autistic students. Specifically, it examines students' acceptance of AI as a complementary support tool alongside human mentors, their previous mentorship experiences, their experiences and perceptions of AI, as well as their deeper visions for future AI integration in mentorship. Chapter 2 presents findings from an exploratory online survey of 203 autistic students, offering insights into their perceptions of AI-augmented mentorship, including perceived benefits, limitations, and areas of concern. Building on these results, Chapter 3 describes a follow-up interview study with 15 participants that explores their current academic and professional experiences typically supported by mentorship and their vision for how AI could support key mentorship activities. Thematic analysis reveals a strong desire for integrated, intelligent tools to facilitate online communication, time management, and emotional well-being, alongside valid concerns regarding the limitations and potential harms of current AI systems. The findings inform design considerations for future AI-supported mentorship and underscore the importance of accessibility and inclusion in the development of such systems. We conclude with recommendations for the design of future mentorship programs that effectively integrate AI to support the unique needs of autistic students in STEM. Further this work discusses the importance of universal, user-centered design for technology."]},{"key":"dc:description.abstractgeneral","label":"General Abstract","values":["Autistic college students pursuing science, technology, engineering, and math (STEM) often face unequal treatment after graduation, including lower rates of employment and pay. These challenges persist despite the availability of college mentorship programs designed specifically to support autistic students. Artificial Intelligence (AI) refers to technology that can perform tasks typically associated with human intelligence—such as understanding language, generating images, or recognizing faces. While many neurodivergent individuals have found AI tools helpful in their daily lives, the use of AI to support mentorship for autistic students in STEM remains largely unexplored. This research explores how AI could help improve mentorship programs for autistic students by examining both potential benefits and concerns. It includes (1) an online survey of 203 autistic students and (2) follow-up interviews with 15 students. The findings highlight students' past experiences with mentorship, their views on AI, and how they imagine AI could be used to support STEM mentorship programs. This work offers important insights for designing future mentorship tools and programs and emphasizes the need for technology that is inclusive, supportive, and accessible to all."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Master of Science"]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["ETD"]},{"key":"dc:title","label":"Title","values":["Understanding the Applications of AI for Autistic STEM Student Mentorship"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Rho, Eugenia Ha Rim"],"dc:contributor.committeemember":["Lee, Sang Won","Jeon, Myounghoon"],"dc:contributor.department":["Computer Science and#38; Applications"],"dc:creator":["Stil, Sophia"],"dc:date.accessioned":["2025-06-28T08:01:37Z"],"dc:date.available":["2025-06-28T08:01:37Z"],"dc:date.issued":["2025-06-27"],"dc:description.abstract":["Autistic college students in STEM face significant challenges in employment, such as low-pay and hiring discrimination compared to their neurotypical peers, despite existing mentorship programs for career development. This study investigates how artificial intelligence (AI) can be leveraged to enhance STEM mentorship for autistic students. Specifically, it examines students' acceptance of AI as a complementary support tool alongside human mentors, their previous mentorship experiences, their experiences and perceptions of AI, as well as their deeper visions for future AI integration in mentorship. Chapter 2 presents findings from an exploratory online survey of 203 autistic students, offering insights into their perceptions of AI-augmented mentorship, including perceived benefits, limitations, and areas of concern. Building on these results, Chapter 3 describes a follow-up interview study with 15 participants that explores their current academic and professional experiences typically supported by mentorship and their vision for how AI could support key mentorship activities. Thematic analysis reveals a strong desire for integrated, intelligent tools to facilitate online communication, time management, and emotional well-being, alongside valid concerns regarding the limitations and potential harms of current AI systems. The findings inform design considerations for future AI-supported mentorship and underscore the importance of accessibility and inclusion in the development of such systems. We conclude with recommendations for the design of future mentorship programs that effectively integrate AI to support the unique needs of autistic students in STEM. Further this work discusses the importance of universal, user-centered design for technology."],"dc:description.abstractgeneral":["Autistic college students pursuing science, technology, engineering, and math (STEM) often face unequal treatment after graduation, including lower rates of employment and pay. These challenges persist despite the availability of college mentorship programs designed specifically to support autistic students. Artificial Intelligence (AI) refers to technology that can perform tasks typically associated with human intelligence—such as understanding language, generating images, or recognizing faces. While many neurodivergent individuals have found AI tools helpful in their daily lives, the use of AI to support mentorship for autistic students in STEM remains largely unexplored. This research explores how AI could help improve mentorship programs for autistic students by examining both potential benefits and concerns. It includes (1) an online survey of 203 autistic students and (2) follow-up interviews with 15 students. The findings highlight students' past experiences with mentorship, their views on AI, and how they imagine AI could be used to support STEM mentorship programs. This work offers important insights for designing future mentorship tools and programs and emphasizes the need for technology that is inclusive, supportive, and accessible to all."],"dc:description.degree":["Master of Science"],"dc:format.medium":["ETD"],"dc:identifier.other":["vt_gsexam:44234"],"dc:identifier.uri":["https://hdl.handle.net/10919/135740"],"dc:language.iso":["en"],"dc:publisher":["Virginia Tech"],"dc:rights":["In Copyright"],"dc:rights.uri":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["human-centered computing","accessibility"],"dc:title":["Understanding the Applications of AI for Autistic STEM Student Mentorship"],"dc:type":["Thesis"],"thesis:degree_discipline":["Computer Science & Applications"],"thesis:degree_level":["masters"],"thesis:degree_name":["Master of Science"],"thesis:institution_name":["Virginia Polytechnic Institute and State University"]},"updated_at":"2026-07-24T05:56:30Z"}