Page 74 - International Perspectives on Effective Teaching and Learning in Digital Education
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Vesna Ferk Savec and Sanja Jedrinović
point in global sustainability governance, as this dual framework creates a
comprehensive paradigm for both national implementation and internation-
al cooperation on sustainable development initiatives that require profound
changes in governments, civil society, science and business (Bocken et al.,
16; Sachs et al., 19, 1). Its far-reaching impact can already be seen in its
integration into national development plans, corporate sustainability strate-
gies and research agendas in various disciplines (Rosa et al., 19; Schroeder
et al., 19).
Vinuesa et al. () argues that as artificial intelligence (AI) evolves and
integrates into different aspects of society, the economy and governance, its
potential to both accelerate and hinder progress towards the realisation of
the SDGs becomes increasingly clear. To provide an overview of the gener-
al areas of positive and negative impacts of AI, they categorised the SDGs
into three groups, corresponding to the three pillars of sustainable develop-
ment, namely Society pilar (SDG 1-SDG 7, SDG 11, SDG 16), Economy pilar (SDG
8-SDG 1, SDG 1, SDG 17), and Environment pilar (SDG 13-SDG 15). The most
positive impacts of AI were identified in Environmental pilar (93%), followed
by Society pilar (8%) and Economy pilar (7%). In contrast, the most nega-
tive impacts were identified in Society pilar (38%), Economy (33%) and Envi-
ronment (3%) (Vinuesa et al., ).
The usefulness of AI in many areas of sustainable development has
sparked interest in exploring its role in higher education. Crompton and
Burke's (3) review of the literature on the use of AI tools in higher educa-
tion points to the rapid increase in the implementation of AI in higher edu-
cation, which has been used primarily at the undergraduate and graduate
levels for the following purposes: (1) Assessment/Evaluation (e.g. automat-
ed assessment, test creation, feedback, review of students' online activities,
evaluation of educational resources), () Predicting (e.g. academic perfor-
mance, project topics, dropout, career decisions, innovation ability, etc.),
(3) AI Assistant (e.g. virtual agents, chatbot assistance, general assistance),
(4) Intelligent Tutoring System (adaptive instructional systems that incor-
porate the use of AI techniques and pedagogical methods) and (5) Man-
aging Student Learning (e.g. learning analysis, identification of learning
patterns, curriculum sequencing, instructional design, analysis of teaching
effects, clustering of students' personal characteristics, etc.). Almost 5% of
the studies were conducted in the fields of language learning, computer
science, management and engineering, while only a few studies were re-
ported in the fields of maths, education, medicine and music (Crompton &
Burke, 3).
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