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To continue to stay relevant in the era of Industry Revolution 4.0 (IR4.0) alongside the unprecedented disruption of COVID-19, the importance of lifelong learning is indisputable though this concept has existed for decades. In this context, open and distance learning (ODL) institutions are urged to re-think and re-design their online learning support systems that inculcate self-regulated lifelong learning (SR3Ls) in their learners to be adaptable and resilient for the post-pandemic economy. The purpose of this paper is to develop a SR3Ls model, namely SR3Ls model by utilising the collective opinions of a panel of experts to determine the key domains and attributes.

A 2-round Delphi consensus study was conducted with 39 experts from five countries. The mean, standard deviation (SD), inter-quartile range (IQR) and the ratio of experts assigned score of 4 or greater were used as the basis of consensus assessment with criteria set at mean = 3.0, SD = 1.5, IQR = 1, ratio on score 4 or greater at = 75%. The questionnaire consisted of 5-point Likert-type scale rating the importance level of each attribute combined with open-ended questions.

This paper presented the findings of the first round of Delphi consensus study. For the first round, the experts were asked to evaluate 31 key attributes of SR3Ls model under five domains. The findings revealed that there were five key attributes to be eliminated from the list, while there were seven attributes identified as the key attributes with highest consensus. There were additional attributes suggested by the Delphi panel to be added in the second round of evaluation.

This international consensus-based SR3Ls model serves as an important benchmark for ODL institutions across the regions in developing meaningful and relevant online learning support systems for their learners to adopt SR3Ls attributes in order to meet the dynamic market demands.

Few resources exist to incorporate principles of modular approach to course design. This research aimed to help instructors by presenting principles for practical and empirically informed modular course design in engineering education.

In the first phase, a systematic literature review was completed to identify categories addressing a modular course design. Search and screening procedures resulted in 33 qualifying articles describing the development of a modular course. In the second phase, 6 expert interviews were conducted to elaborate on the identified categories.

Guided by the interview results and the ADDIE (Analyze, Design, Develop, Implement, and Evaluate) course design model, the categories were compiled into six design principles. To present the design principles in relation to the guiding principles of modular approach, an overarching conceptual model was developed.

Here, we present our innovation; a foundation for an evidence-based systematic approach to modular course design. Implications have value for supporting flexibility and autonomy in learning.

This innovative practice paper explains how a student-produced podcast is used as an educational tool to showcase leadership and ethics. It illustrates how podcasting provides a unique pedagogical experience for students to engage with leadership themes in a way that is accessible, practical, and relevant. In this example, the podcast episodes become an innovative teaching resource, while the creation of it provides an experiential learning opportunity for the student hosts. In creating the work, students develop essential critical thinking skills, and the students who engage with the podcast are introduced to valuable leadership concepts.

AI-based robo-advisory (RA) represents a FinTech application that is already replacing retail investment advisors. In private banking (PB), clients also increasingly expect service provision across different digital channels, but with a higher degree of personalization. Hence, the present study investigates the impact of intelligent RA on the PB investment advisory process to derive both process (re)design knowledge and strategic guidance for artificial intelligence (AI) usage for PB investment advisory.

The present study applies an AI process impact analysis approach by decomposing AI-based RA into three AI application types: conversational agent, customer segmentation and predictive analytics. The analysis results along a reference PB investment advisory process reveal sub-process transformations which are applied for process redesign integrating AI.

The study results imply that AI systems (1) enable seamless client journeys, (2) increase advisor flexibility, (3) support the client–advisor relationship by applying an omnichannel approach and (4) demand advisor skills to be augmented with technical and statistical knowledge.

The research study contributes (1) an AI process impact analysis approach, (2) derives process (re)design knowledge for AI deployment and (3) develops strategic guidance for AI usage in PB investment advisory.