Abstract
This chapter explores the transformative potential of integrating conversational AI agents, particularly chatbots, into the educational landscape. Drawing on the insights from the EDUChat project, the findings reveal a notable surge in student expectations following the return to face-to-face teaching. The data reflects an increased desire for personalised and instantaneous support, indicating a paradigmatic evolution in educational requirements. Due to the global shift to online education in response to the COVID-19 pandemic, this chapter recognises identified disparities in digital access and infrastructure, emphasising the vital role of communication, student engagement, and innovative teaching methodologies. It highlights the need for interventions like improved infrastructure and expanded online resources to reshape post-pandemic educational systems, underscoring the importance of equitable resource distribution and proactive strategies.
TopIntroduction
The global shift to online education during the COVID-19 pandemic has amplified existing challenges in both developed and developing nations. Factors like limited resources, digital illiteracy, and inadequate infrastructure have significantly hindered effective distance learning. This has led to increased educational disparities, especially for vulnerable groups. Addressing these issues requires prompt interventions, including improved infrastructure, expanded online resources, and active community engagement. These measures are crucial for reshaping post-pandemic educational systems and mitigating prevailing inequalities (Tadesse & Muluye, 2020).
There have been several studies that explored the impact of COVID-19 on global education, particularly in developing countries, revealing challenges in pedagogy and platform suitability, exacerbated by limited internet access and costly data packages. These studies found that policy intervention is crucial to address accessibility and affordability issues. The challenges covered wide research areas including effective online pedagogy, authentic assessments, and tool customization for diverse economic backgrounds. The main lessons learned underscore the necessity of orienting teachers and learners on online tools, advocating their continued use post-pandemic for enhanced teaching and learning (Pokhrel & Chhetri, 2021).
In this chapter, we thoroughly examine the complex challenges associated with modern educational approaches, with a specific focus on the need to improve the flexibility of learning systems in response to changing requirements and the widespread shift to remote education. Our analysis includes in-depth discussions on crucial topics, such as how the move to remote learning significantly affects student engagement, accessibility, and personalised learning. We also closely analyse the significant challenges that educators face in adapting to the rapidly changing educational landscape. The chapter further explores the nuanced investigation of students' changing expectations, preferences, and behaviours in the post-pandemic era, highlighting the increasing desire for personalised, flexible, and easily accessible learning experiences. Lastly, we emphasise the critical importance of enhanced support and guidance for students in the virtual learning environment.
Key Terms in this Chapter
Human-Centred AI (HCAI): An approach to artificial intelligence development that prioritises human needs, experiences, and values. It focuses on creating AI systems that align with human goals and enhance well-being.
Generative AI: Artificial intelligence systems capable of creating new content, ideas, or responses autonomously. It involves machines generating original and contextually relevant outputs.
Change Management: A methodical roadmap for guiding individuals, teams, and organisations through transitions from their present situation to an envisioned future. It emphasises effective planning, implementation, and long-term support for change initiatives.
Great Reset: A term often used to describe a global effort to address and reshape economic, social, and environmental systems, typically in response to significant challenges or crises.
Large Language Models (LLMs): Cutting-edge AI systems that excel at comprehending and producing human-quality text across a vast spectrum of topics. They represent a significant leap forward in natural language processing (NLP) and are increasingly being adopted for diverse language-based tasks.
Community Of Inquiry: A theoretical framework in online education that emphasises collaborative exploration and construction of knowledge. It comprises three essential elements: cognitive presence (intellectual inquiry), social presence (sense of community), and teaching presence (facilitation of learning).
Emergency Remote Education (ERE): A temporary shift to online learning in response to unexpected events, such as natural disasters or pandemics. It differs from planned online education as it is a rapid and temporary measure.
Distance Learning: A mode of education where students and instructors are separated geographically, often relying on technology for communication. It encompasses various forms, including online courses, correspondence programs, and virtual classrooms.
E-Servicescape: The online environment or digital space where electronic services are provided. It involves the design and presentation of digital interfaces to enhance user experiences in online service delivery.
Social Presence: The degree to which participants in an online learning environment feel connected and engaged with one another. It encompasses a sense of community, communication warmth, and interpersonal interaction.
Trustworthy AI: Artificial intelligence systems that exhibit transparency, fairness, accountability, and reliability in their operations, earning the trust of users and minimising negative societal impacts.
Conversational AI: Artificial intelligence systems designed to engage in natural language conversations with users. They are often used in chatbots, virtual assistants, and other applications.
Blended Learning: An instructional method that integrates conventional in-person teaching with online learning activities. Its aim is to maximise the advantages of both face-to-face and digital approaches, thereby improving the overall learning experience.
Machine Learning: An integral part of artificial intelligence (AI) focused on creating algorithms that empower computers to learn from patterns in data, enabling them to make predictions or decisions without explicit programming.
Two Sigma Effect: A term in education referring to the significant impact of personalised, data-driven approaches on improving learning outcomes, akin to the measurable effect of medical interventions in healthcare.
Teaching Presence: Within the Community of Inquiry framework, the element that reflects the role of the instructor in designing and guiding the learning process. It includes instructional design, facilitation, and direct instruction.
Human-Computer Interaction (HCI): The study and design of how humans interact with computer systems, including interfaces, software, and hardware, with the aim of enhancing usability and user experience.