CASExplorer: A Conversational Academic and Career Advisor for College Students

Lee, Terri; Zhu, Tianjie; Liu, Sophia; Trac, Linh; Huang, Zizhen; Chen, Yu

doi:10.1145/3490355.3490368

Cited by 5 publications

(1 citation statement)

References 20 publications

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“…0 0.00 0.00 Using a bilingual chatbot that communicates with students and uses NLP processing and a purpose-built knowledge base, AI is applied to academic advice. 0 0.00 0.00 Lee et al (2021) Employing chatbots to provide academic and career advising services, helping students select majors and find related careers. 0 0.00 0.00…”

Section: Lizarraga Et Al (2022)mentioning

confidence: 99%

Robo academic advisor: Can chatbots and artificial intelligence replace human interaction?

Thottoli,

Alruqaishi,

Soosaimanickam

2024

CONT ED TECHNOLOGY

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Purpose: Chatbots and artificial intelligence (AI) have the potential to alleviate some of the challenges faced by humans. Faculties frequently swamped with teaching and research may find it difficult to act in a parental role for students by offering them individualized advice. Hence, the primary purpose of this study is to review the literature on chatbots and AI in light of their role in auto-advising systems. The authors aimed to gain insights into the most pertinent topics and concerns related to robo academic advisor and identify any gaps in the literature that could serve as potential avenues for further research. Design/methodology/approach: The research employs a systematic literature review and bibliometric techniques to find 67 primary papers that have been published between 1984 and 2023. Using the Scopus database, the researchers built a summary of the literature on chatbots and AI in academic advice. Findings: Chatbot applications can be a promising approach to address the challenges of balancing personalized student advising with automation. More empirical research is required, especially on chatbots and other AI-based advising systems, to understand their effectiveness and how they can be integrated into educational settings. Research limitations/implications: This research’s sample size may restrict its findings’ generalizability. Furthermore, the study’s focus on chatbots may overlook the potential benefits of other AI technologies in enhancing robo academic advising systems. Future research could explore the impact of robo academic advisors in diverse societal backgrounds to gain a more comprehensive understanding of their implications. Practical implications: Higher educational institutions (HEIs) should establish a robo academic advising system that serves various stakeholders. The system’s chatbots and AI features must be user-friendly, considering the customers’ familiarity with robots. Originality/value: This study contributes to a better understanding of HEIs’ perceptions of the adoption of chatbots and AI in academic advising by providing insightful information about the main forces behind robo academic advising, illuminating the most frequently studied uses of chatbots and AI in academic advising.

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Section: Lizarraga Et Al (2022)mentioning

confidence: 99%

Robo academic advisor: Can chatbots and artificial intelligence replace human interaction?

Thottoli,

Alruqaishi,

Soosaimanickam

2024

CONT ED TECHNOLOGY

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Integrating Knowledge into Conversational Agents for Worker Upskilling

Alonso,

Dessí,

Meloni

et al. 2023

Artificial Intelligence in Manufacturing

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The labor market is a key part of an economy. Several existing online platforms allow the upload of resumes and the search for a job. One of their limitations, however, is that obtaining the best opportunity can be hard because certain jobs need some experiences, abilities, and features that an applicant might not know. The recent diffusion and employment of conversational agents definitely have proven to benefit this kind of issue. For example, ChatGPT has shown impressive outcomes in different domains and for a variety of tasks. It has weaknesses, although, related to the veracity of the responses it generates, which might deceive the user interacting with it. The usage of external domain knowledge is the direction we suggest in this chapter. Several lexical databases and taxonomies have already been collected and designed by different organizations. We illustrate a list of such resources and provide a solution that integrates conversational agents with relevant information extracted from one of such resources showing the benefits and the impact that our proposal can generate.

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