Abstract

1. Introduction

2. Defining the territory

3. Methodology

4. Results

5. Joint discussion

6. Concluding remarks

Declaration of competing interest

Acknowledgements

References

Abstract

     The proliferation of AI in many aspects of human life—from personal leisure, to collaborative professional work, to global policy decisions—poses a sharp question about how to prepare people for an interconnected, fast-changing world which is increasingly becoming saturated with technological devices and agentic machines. What kinds of capabilities do people need in a world infused with AI? How can we conceptualise these capabilities? How can we help learners develop them? How can we empirically study and assess their development? With this paper, we open the discussion by adopting a dialogical knowledge-making approach. Our team of 11 co-authors participated in an orchestrated written discussion. Engaging in a semi-independent and semi-joint written polylogue, we assembled a pool of ideas of what these capabilities are and how learners could be helped to develop them. Simultaneously, we discussed conceptual and methodological ideas that would enable us to test and refine our hypothetical views. In synthesising these ideas, we propose that there is a need to move beyond AI-centred views of capabilities and consider the ecology of technology, cognition, social interaction, and values.

Introduction

     The appearance of computers in the workplaces at the turn of the 21st century has added ‘algorithmic thinking’ and ‘computing literacy’ to the repertoire of thinking skills and literacies that have been seen as essential for successful functioning and employment in society (Knuth, 1985; Papert, 1972; Sloan & Halaris, 1985). The proliferation of personal computers and other digital devices in people’s everyday lives raised the need for different kinds of skills and literacies, such as ‘ICT skills’, ‘media literacy’ and ‘digital literacy’ (Markauskaite, 2005, 2006). The recent emergence of big data, machine learning, robotics and Al gave the birth to ‘data literacy’, ‘computational thinking’, ‘AI literacy’ and other new skills (Bull, Garofalo, & Hguyen, 2020; Long & Magerko, 2020; Mandinach & Gummer, 2013). Simultaneously, the increasing interconnectivity, complexity, and fast changes in knowledge and skills needed for everyday life and jobs have shifted the attention from technology-centred skills and literacies to a broader set of generic competencies, such as creativity, analytical thinking, active self-driven learning, and global citizenship (World Economic Forum, 2018, 2020).

Results

     Our individual perspectives on the capabilities for an AI-infused world ranged from more individual, cognitively oriented views to more relational, socially oriented perspectives. We use this dimension as a guide to sequence our contributions, starting from the perspectives that emphasise individuals and moving towards broader, relational conceptualisations.

     4.1. Using AI to become an agentic learner: A self-regulated learning perspective (Dragan Gašević, DG)
4.1.1. Q1: What kind of capabilities do people need in a world with Al?

     Developments in AI accelerate technological change in workplaces and demands for continuous learning, upskilling, and reskilling. To maintain job relevance and support future career transitions in a world with AI, individuals will require highly developed self-regulated learning (SRL) skills (Winne et al., 2017). These are not just important for matters related to labour markets but also for other aspects of life such as personal finances, health, culture, and climate. SRL skills play a critical role in all facets of human learning and development. For instance, SRL underpins how learners navigate and operate on online information, form queries to search information on the Web or social media, and scan and assemble information. At each step, learners decide what information is relevant and judge how it supports achievement of their learning goals (Dunlosky & Thiede, 2013). The need for SRL skills is even more acutely emphasised in the age of AI due to two prominent reasons: (i) the need to adapt (re- or up-skill) frequently due to speed of job and life changes; and (ii) the need to maintain agency in decision making while working AI systems.

Self-regulated learning (SRL) is crucial for academic success; therefore support, to enhance and maintain SRL skills is important. In blended adult education, the heterogeneity of adults creates diversity in SRL abilities, which makes it necessary to provide tailored support. Conducting latent profile analyses for a sample of 213 blended adult students, we identified three profiles, namely high, low, and moderate SRL profiles which prove differences in SRL strategy use and imply tailored SRL support. Through multivariate analysis of variance (MANOVA) and multinomial logistic regression, we further explore the differences in SRL between the profiles and the extent to which the students’ personal background characteristics and achievement motivations predict their profile membership. The three profiles differ significantly in terms of the scores of all SRL subscales. Furthermore, only achievement motivation – more specifically, attainment and utility value – predicts profile membership. These results inform educational practice about opportunities for supporting and enhancing SRL skills. Anticipating attainment and utility value, time management, and collaboration with peers are all recommended. More specifically, teachers can, for example, use authentic tasks and examples during the learning process or be a role model regarding online interaction and information sharing.

Introduction

Blended learning environments combine face-to-face and online learning activities that are meant to complement each other (Boelens, Van Laer, De Wever, & Elen, 2015). These environments allow students considerable autonomy, which requires self-regulated learning (SRL) for individuals to succeed (Peverly, Brobst, Graham, & Shaw, 2003). Although blended learning environments are autonomous, tailored support for adult students in developing and maintaining their SRL skills should be provided. Since adult students are heterogeneous regarding their previous life, work, and educational experiences, they are diverse in their SRL skills (Barnard-Brak, Lan, & Paton, 2010), which makes a one-size-fits-all approach insufficient for supporting adult students. Optimisation and personalisation of the support of students is crucial for their learning (Authors, 2018b). To adjust the support they provide, teachers should have a clear perspective on students’ current SRL. However, creating a rich assessment of adult students’ SRL in blended learning environments is challenging because (1) existing research on the SRL of adult students in blended learning environments and how to support it is lacking, (2) research on SRL of students in contexts other than blended adult education is not generalisable to blended adult education due to the context specificity of SRL, and (3) in blended learning environments, teachers have limited time to observe their students individually. While blended learning environments allow for the individualisation of education and support, it can become challenging to provide individualised SRL support for each student considering the teachers’ time and effort required to do so. The current study consequently examines how to support teachers in gaining information about their students’ individual SRL needs in order to provide personalised support at an achievable level.

مقاله سال 2016

شبکه های اجتماعی به شکل جدید ارتباطات تبدیل شده اند که به دانشجویان این اجازه را می دهند که مطالبی را به اشتراک گذاشته و همکاری نمایند. آنها در این مفهوم به نیروهای با مهارت های یادگیری خود ساماندهی محلق شده اند. این مقاله تجربه ای در دانشگاه سانتیگو د کامپوستلا مطرح می کند تا تحلیل کند که چگونه یادگیری خود ساماندهی در دروه ای رشد می یابد و از شبکه اجتماعی استفاده می کند. این تحقیق از موارد زیر استفاده کرد: 1-پرسشنامه راهبردهای انگیزشی برای یادگیری توسط پینتریچ و 2-راهبردهای تحلیل اجتماعی درون چارچوب تحلیل یادگیری. نتایج نشان می دهد که آموزش دانشجویان را تشویق می کند تا با هم در تعامل بوده و محیط غنی برای توسعه مهارت های یادگیری خود ساماندهی ایجاد کنند.