Listen and learn: Research paper Presentations with similar research topics are each assigned to round tables where hour-long discussions take place. Roundtables are intended to be more collaborative discussions about research.
This is presentation 2 of 3, scroll down to see more details.
Other presentations in this group:
Our research primarily embodies a constructivist learning perspective and integrates pedagogical theories of experiential learning. We aim to foster active engagement and knowledge construction among students by incorporating hands-on experiences, practical exercises, case studies, and real-world simulations into AI-focused social media marketing courses. This aligns with the constructivist view that learners construct knowledge by actively engaging with content and experiences.
Additionally, our research framework draws on technology-enhanced learning theories, emphasizing the role of technology, specifically artificial intelligence, in enhancing educational experiences. We consider how AI-driven tools and algorithms can facilitate personalized learning and provide valuable insights to students, aligning with the principles of technology-enhanced learning.
Furthermore, our investigation is influenced by curriculum development theories, as we seek to identify effective strategies for curriculum design that bridge the gap between theoretical knowledge and practical application. This aligns with curriculum development principles that emphasize relevance and applicability in preparing students for real-world contexts.
In summary, our research encompasses a constructivist learning perspective, experiential learning theories, technology-enhanced learning theories, and curriculum development principles to investigate and enhance AI-focused education in social media marketing.
Proposed Methodology
To address the research questions concerning the integration of AI-driven social media marketing education into undergraduate curricula and its effectiveness in preparing students for AI-driven marketing roles, we will employ a mixed-methods research approach. This approach combines both qualitative and quantitative research methods to provide a comprehensive understanding of the phenomenon.
Data Collection
To gather quantitative data on the impact of AI-focused courses on students' preparedness and the effectiveness of pedagogical approaches, structured survey questionnaires will be distributed to undergraduate students enrolled in social media marketing courses. The survey will include questions related to their perceptions of AI in marketing education and their self-assessment of AI-related competencies.
Qualitative data will be collected through in-depth interviews with educators and experts in the field of social media marketing and AI. These interviews will explore their experiences, insights, and best practices in teaching AI-related content and assessing students' preparedness.
Sampling
A purposive sampling technique will be used to select undergraduate students from various academic institutions with AI-focused social media marketing courses. The sample will encompass a diverse range of students to ensure a comprehensive representation. Educators and experts will be selected based on their expertise in AI and social media marketing education. Snowball sampling may be employed to identify additional participants through recommendations from initial interviewees.
Data Analysis
Survey data will be analyzed using descriptive statistics to examine students' perceptions and self-assessments. Inferential statistics, such as regression analysis, may be employed to determine the relationship between the effectiveness of pedagogical approaches and students' preparedness.
Thematic analysis will be conducted on interview transcripts to identify common themes, patterns, and best practices related to teaching AI in social media marketing. Qualitative data analysis software, such as NVivo, will be utilized to facilitate the coding and organization of qualitative data.
We have specific expectations for our study:
Effective Teaching Methods: We anticipate identifying effective pedagogical approaches for teaching AI-related competencies in social media marketing to undergraduates. This information will guide educators in delivering AI-focused content more efficiently.
Impact on Student Preparedness: We expect to assess the impact of AI-focused courses on students' preparedness for AI-driven marketing roles. Insights into how these courses benefit students will be valuable for curriculum development.
Best Practices for Curriculum Development: We plan to uncover best practices for the development of innovative AI-focused social media marketing programs in education. These practices will assist institutions in enhancing their curriculum offerings.
While our results are not yet finalized, we believe that the data we collect through surveys and interviews will provide meaningful insights into these areas, benefiting the field of education and technology.
Our study holds significant educational and scientific importance for both the field of education technology and the attendees of ISTE.
Educational Importance
1. Curriculum Enhancement: Our research aims to identify effective teaching methods and best practices for integrating AI-driven social media marketing education into undergraduate programs. This information is invaluable for educators and institutions looking to enhance their curriculum and prepare students for AI-driven marketing roles.
2. Student Preparedness: Understanding the impact of AI-focused courses on students' preparedness for AI-driven roles is crucial. This knowledge ensures that graduates are well-equipped to excel in the modern job market, aligning education with industry demands.
3. Innovation in Education: By uncovering best practices for curriculum development and innovative program design, our study can foster innovation in education. Attendees will gain insights into cutting-edge approaches that can position their institutions as leaders in providing relevant and impactful education.
Scientific Importance
1. Research Gap Addressed: Our research addresses a notable research gap in the field of AI-focused undergraduate education in marketing. By investigating this area empirically, we contribute to the existing body of knowledge.
2. Evidence-Based Recommendations: The study's findings will provide evidence-based recommendations, enhancing the quality of education in the context of AI and social media marketing. This scientific rigor ensures the credibility of our research.
3. Impact on Industry: As AI continues to shape the marketing landscape, our study's insights will help bridge the gap between academia and industry. Attendees will gain knowledge that can directly impact their institutions and students, making them more competitive in the job market.
Bughin, J., Doogan, J., & Vetvik, O. J. (2017). Artificial intelligence—the revolution hasn’t happened yet. Harvard Business Review, 25.
Boddu, R. S. K., Santoki, A. A., Khurana, S., Koli, P. V., Rai, R., & Agrawal, A. (2022). An analysis to understand the role of machine learning, robotics, and artificial intelligence in digital marketing. Materials Today: Proceedings, 56, 2288-2292.
Gupta, A., Jain, A., & Kumar, A. (2020). Artificial intelligence: A boon for social media marketing. In Advances in Artificial Intelligence (pp. 461-471). Springer.
Holliman, G., Rowley, J., & Wong, Y. (2020). Artificial intelligence: A literature review and research agenda. International Journal of Information Management, 50, 51-62.
Pew Research Center. (2021). Social Media Fact Sheet. Retrieved from https://www.pewresearch.org/internet/fact-sheet/social-media/
Simay, A. E., Wei, Y., Gyulavári, T., Syahrivar, J., Gaczek, P., & Hofmeister-Tóth, Á. (2023). The e-WOM intention of artificial intelligence (AI) color cosmetics among Chinese social media influencers. Asia Pacific Journal of Marketing and Logistics, 35(7), 1569-1598.
Sivasankar, G. A. (2022). Study of blockchain technology, AI, and digital networking in metaverse. IRE Journals, 5(8), 110-115.
Smith, A. N. (2019). Social media marketing. In The SAGE Encyclopedia of the Internet (pp. 972-974). SAGE Publications.
Srivastava, R. K., Singh, R. K., & Pant, P. (2018). A review of artificial intelligence techniques in social media. Materials Today: Proceedings, 5(1), 4643-4647.
Yadav, M. S., & Rahman, M. S. (2020). Artificial intelligence, marketing, and artificial intelligence-based marketing: Implications for firms and markets. Journal of the Academy of Marketing Science, 48(1), 7-10.
Return
Trips and Tours