Generative AI in Higher Education Constituent Relationship Management (CRM): Opportunities, Challenges, and Implementation Strategies
This research explores opportunities for generative artificial intelligence (GenAI) in higher education constituent (customer) relationship management (CRM) to address the industry’s need for digital transformation driven by demographic shifts, economic challenges, and technological advancements. Us...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Computers |
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| Online Access: | https://www.mdpi.com/2073-431X/14/3/101 |
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| author | Carrie Marcinkevage Akhil Kumar |
| author_facet | Carrie Marcinkevage Akhil Kumar |
| author_sort | Carrie Marcinkevage |
| collection | DOAJ |
| description | This research explores opportunities for generative artificial intelligence (GenAI) in higher education constituent (customer) relationship management (CRM) to address the industry’s need for digital transformation driven by demographic shifts, economic challenges, and technological advancements. Using a qualitative research approach grounded in the principles of grounded theory, we conducted semi-structured interviews and an open-ended qualitative data collection instrument with technology vendors, implementation consultants, and HEI professionals that are actively exploring GenAI applications. Our findings highlight six primary types of GenAI—textual analysis and synthesis, data summarization, next-best action recommendations, speech synthesis and translation, code development, and image and video creation—each with applications across student recruitment, advising, alumni engagement, and administrative processes. We propose an evaluative framework with eight readiness criteria to assess institutional preparedness for GenAI adoption. While GenAI offers potential benefits, such as increased efficiency, reduced costs, and improved student engagement, its success depends on data readiness, ethical safeguards, and institutional leadership. By integrating GenAI as a co-intelligence alongside human expertise, HEIs can enhance CRM ecosystems and better support their constituents. |
| format | Article |
| id | doaj-art-b53e6e9d8bef47ecad98f069208c8262 |
| institution | Kabale University |
| issn | 2073-431X |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computers |
| spelling | doaj-art-b53e6e9d8bef47ecad98f069208c82622025-08-20T03:43:15ZengMDPI AGComputers2073-431X2025-03-0114310110.3390/computers14030101Generative AI in Higher Education Constituent Relationship Management (CRM): Opportunities, Challenges, and Implementation StrategiesCarrie Marcinkevage0Akhil Kumar1Smeal College of Business, Pennsylvania State University, University Park, PA 16802, USASmeal College of Business, Pennsylvania State University, University Park, PA 16802, USAThis research explores opportunities for generative artificial intelligence (GenAI) in higher education constituent (customer) relationship management (CRM) to address the industry’s need for digital transformation driven by demographic shifts, economic challenges, and technological advancements. Using a qualitative research approach grounded in the principles of grounded theory, we conducted semi-structured interviews and an open-ended qualitative data collection instrument with technology vendors, implementation consultants, and HEI professionals that are actively exploring GenAI applications. Our findings highlight six primary types of GenAI—textual analysis and synthesis, data summarization, next-best action recommendations, speech synthesis and translation, code development, and image and video creation—each with applications across student recruitment, advising, alumni engagement, and administrative processes. We propose an evaluative framework with eight readiness criteria to assess institutional preparedness for GenAI adoption. While GenAI offers potential benefits, such as increased efficiency, reduced costs, and improved student engagement, its success depends on data readiness, ethical safeguards, and institutional leadership. By integrating GenAI as a co-intelligence alongside human expertise, HEIs can enhance CRM ecosystems and better support their constituents.https://www.mdpi.com/2073-431X/14/3/101generative artificial intelligenceGenAIcustomer relationship managementconstituent relationship managementCRMhigher education |
| spellingShingle | Carrie Marcinkevage Akhil Kumar Generative AI in Higher Education Constituent Relationship Management (CRM): Opportunities, Challenges, and Implementation Strategies Computers generative artificial intelligence GenAI customer relationship management constituent relationship management CRM higher education |
| title | Generative AI in Higher Education Constituent Relationship Management (CRM): Opportunities, Challenges, and Implementation Strategies |
| title_full | Generative AI in Higher Education Constituent Relationship Management (CRM): Opportunities, Challenges, and Implementation Strategies |
| title_fullStr | Generative AI in Higher Education Constituent Relationship Management (CRM): Opportunities, Challenges, and Implementation Strategies |
| title_full_unstemmed | Generative AI in Higher Education Constituent Relationship Management (CRM): Opportunities, Challenges, and Implementation Strategies |
| title_short | Generative AI in Higher Education Constituent Relationship Management (CRM): Opportunities, Challenges, and Implementation Strategies |
| title_sort | generative ai in higher education constituent relationship management crm opportunities challenges and implementation strategies |
| topic | generative artificial intelligence GenAI customer relationship management constituent relationship management CRM higher education |
| url | https://www.mdpi.com/2073-431X/14/3/101 |
| work_keys_str_mv | AT carriemarcinkevage generativeaiinhighereducationconstituentrelationshipmanagementcrmopportunitieschallengesandimplementationstrategies AT akhilkumar generativeaiinhighereducationconstituentrelationshipmanagementcrmopportunitieschallengesandimplementationstrategies |