Conference Agenda

The escalation in capabilities of Large Language Models has triggered urgent discussions about their implications for tertiary education, particularly regarding how they might facilitate academic misconduct in graded engineering coursework. However, graduate research education — where a student works closely with a supervisor over years to develop both implicit and explicit research skills — has received comparatively less attention in this discussion. This paper seeks to develop this discourse by presenting targeted case studies that explore the opportunities and threats posed by artificial intelligence to engineering doctoral education. For instance, using a specimen exercise from a PhD-level research skills module, we demonstrate how artificial intelligence tools can now deeply penetrate research workflows in technical computing and scripting. We likewise investigate the capabilities of chatbot tools to assist engineering PhD candidates with the broader research skills central to their training and development. These include writing and proofreading theses and research papers, producing data visualizations, simulating peer review processes, and preparing scientific diagrams. By evaluating the capabilities and limitations of extant artificial intelligence in these areas, we can discuss both the potential benefits and ethical concerns of doctoral students engaging with such assistance.

The integration of gamification in educational settings has shown promise in enhancing student engagement and learning outcomes. However, educators often face significant challenges in designing and implementing effective gamification strategies due to lack of expertise, time constraints, and limited resources. This paper introduces a novel framework leveraging generative artificial intelligence (AI) to assist educators in creating engaging gamified learning experiences. The AI-Assisted Gamification Framework aims to simplify the process of designing, implementing, and evaluating gamification solutions across various educational domains, addressing common challenges faced by educators such as lack of expertise and resource constraints. To demonstrate its practical application, the paper presents a case study based on a first-year university business analysis course. Additionally, it provides a comprehensive prompting guide with sample AI prompts for each stage of the gamification design process. The research discusses potential benefits of the framework, including time efficiency, enhanced creativity, and improved scalability, while also addressing challenges such as the need for AI literacy among educators and ethical considerations. This structured methodology empowers educators to create impactful gamification experiences with AI assistance, potentially enhancing student engagement and learning outcomes while overcoming common implementation barriers in educational gamification.

This paper aims to propose an implementation framework for the adoption and management of Open Educational Resources (OER), focusing on their lifecycle, as well as the integration of AI for supporting educators in their classification of the created content, the creation of tutoring agents for the learning process and learners in deepening their learning experience and exploitation. We explore the incentives for educators, connections to educational programs, and propose a participatory design model for effective implementation. The application of this framework to the Graasp.org learning experience platform and its associated open OER library is also discussed, along with future implementation strategies.

Abstract:

The rapid advancements in Artificial Intelligence (AI) have revolutionized various sectors, including education. This research explores the potential of AI-enabled re-skilling and upskilling to address the evolving educational needs in the era of digital transformation. The study focuses on how AI technologies can be leveraged to enhance learning experiences, personalize education, and prepare learners for the dynamic job market.

In the context of Jordanian Small and Medium-sized Enterprises (SMEs), the integration of AI in Social Media Marketing (SMM) serves as a case study to understand the broader implications of AI in education. This research delves into the profound impact of AI-driven SMM on marketing performance, customer engagement, and business growth. By examining the strategic implementations and challenges faced by Jordanian SMEs, the study offers valuable insights into the transformative potential of AI in educational settings.

The research employs a mixed-methods approach, combining qualitative interviews with SME owners and marketing managers, and quantitative surveys. The findings highlight AI's capability to analyze vast datasets, predict trends, and tailor interactions, which can be translated into educational contexts to enhance learning outcomes. The study also identifies the barriers to AI adoption and provides practical strategies for overcoming these challenges.

Overview and Objective:

The rapid advancements in Artificial Intelligence (AI) have revolutionised various sectors, including education. This research explores the potential of AI-enabled re-skilling and upskilling to address the evolving educational needs in the era of digital transformation. The objective is to investigate how AI technologies can be leveraged to enhance learning experiences, personalise education, and prepare learners for the dynamic job market.

In the context of Jordanian Small and Medium-sized Enterprises (SMEs), the integration of AI in Social Media Marketing (SMM) serves as a case study to understand the broader implications of AI in education. This research delves into the profound impact of AI-driven SMM on marketing performance, customer engagement, and business growth. By examining the strategic implementations and challenges faced by Jordanian SMEs, the study offers valuable insights into the transformative potential of AI in educational settings.

Key Research Areas:

1. AI in Education:

• Leveraging AI technologies such as machine learning (ML), deep learning (DL), and reinforcement learning (RL) to create personalised and adaptive learning environments.
• Exploring AI’s role in enhancing the quality and accessibility of education through intelligent tutoring systems and personalised feedback mechanisms.

Re-Skilling and Upskilling:

• Implementing AI-driven platforms to continuously update skills and knowledge, ensuring learners stay relevant in a rapidly changing job market.
• Developing frameworks for integrating AI into curricula to support lifelong learning and career development.

Impact on SMEs:

• Understanding the role of AI in transforming marketing strategies and its implications for educational practices.
• Examining the success of AI-driven SMM in Jordanian SMEs to extract lessons applicable to educational contexts.

Strategic Implementations:

• Developing effective strategies for integrating AI in education, drawing lessons from the successful use of AI in SMM.
• Identifying the barriers to AI adoption in educational institutions and proposing solutions to overcome these challenges.

Challenges:

Despite the promising potential of AI in education, several challenges impede its widespread adoption:

1. Technical Challenges:

• Integration of AI systems with existing educational infrastructure can be complex and resource intensive.
• Ensuring data privacy and security in AI-driven educational platforms.

Financial Constraints:

• High initial investment costs for AI technologies can be a significant barrier for educational institutions, especially in developing regions.
• Ongoing maintenance and update costs for AI systems.

Organisational and Human Factors:

• Resistance to change among educators and administrators who may be skeptical of AI’s benefits or fearful of job displacement.
• Need for continuous training and upskilling of educators to effectively utilise AI tools in their teaching practices.

Ethical Considerations:

• Addressing potential biases in AI algorithms to ensure fair and equitable educational outcomes.
• Ensuring transparency and accountability in AI decision-making processes.

Methodology:

This study employs a systematic review and comprehensive analysis methodology to explore the current state and impact of AI-enabled re-skilling and upskilling in education. The systematic review involves a structured process to ensure a thorough and unbiased analysis of existing literature. Key phases of the methodology include:

1. Literature Search:

• Conduct a comprehensive search of academic databases including IEEE Xplore, ACM Digital Library, Scopus, Springer Link, and Google Scholar.
• Use keywords such as "Artificial Intelligence in Education," "Re-Skilling," "Upskilling," "AI-Enabled Learning," "Personalized Learning," "Machine Learning," "Deep Learning," and "Lifelong Learning."

Inclusion and Exclusion Criteria:

• Include peer-reviewed journal articles, conference papers, and book chapters published within the last ten years, focusing on AI applications in re-skilling and upskilling within educational contexts.
• Exclude non-English studies, articles without full-text access, and publications focusing solely on technical aspects without educational context.

Data Extraction and Quality Assessment:

• Use a standardized data extraction form to collect relevant information from each study, including study objectives, methods, key findings, and implications.
• Perform quality assessment based on criteria such as clarity of research questions, appropriateness of methodology, robustness of findings, and relevance to the research questions.

Data Synthesis and Analysis:

• Conduct thematic analysis to identify common themes, patterns, and gaps in the literature.
• Use narrative synthesis to provide a comprehensive summary of the findings, integrating qualitative and quantitative data.

Reporting and Dissemination:

• Compile findings into a structured conference paper, including sections for introduction, methodology, results, discussion, and conclusion.
• Present results at the ITHET 2024 conference to engage with the academic community and foster discussions on the future of AI-enabled re-skilling and upskilling in education.

Expected Outcomes:

The study’s findings highlight AI's capability to analyse vast datasets, predict trends, and tailor interactions, which can be translated into educational contexts to enhance learning outcomes. By addressing the challenges and exploring the opportunities presented by AI, this research aims to guide policymakers, educators, and industry stakeholders in adopting AI technologies to foster a culture of continuous learning and innovation.

Keywords: Artificial Intelligence, Re-Skilling, Upskilling, Education, SMEs, Personalised Learning, Digital Transformation.