In response to the publication of the United Nations’ Sustainable Development Goals (SDGs) in 2015, it has become necessary to develop a means of assessing progress toward their achievement. Included in the 17 goals of the 2030 agenda for sustainable development is SDG 4: Quality Education. This calls on nations to “ensure inclusive and equitable quality education and promote lifelong learning opportunities for all” (Goal 4, 2015).

As part of the organizing team of the Inaugural CIES Symposium in Scottsdale, AZ this past November, we were thrilled to continue the debates about Global Learning Metrics (GLMs) at the recent CIES 2017 Conference in Atlanta, GA. CIES 2017 included a number of Presidential Highlighted Sessions.

Almost any education-related topic seems to turn into an overheated debate, provoking very strong gut reactions and diminishing any hope for productive discussions that engage in careful analysis of contrasting perspectives and forms of evidence. This is certainly the case with International Large Scale Educational Assessments (ILSEAs), like PISA or TIMSS, which lack nuanced discussions and methodic analyses of their role in improving student achievement.

Rolling Deadline

The unprecedented speed of advancements in machine learning (ML), generative artificial intelligence (AI), and large language models (LLM) is rapidly transforming formal and informal educational settings and systems. Educators and learners are grappling with unanticipated and rapidly changing AI that impacts both day-to-day K-12 classroom practices and the use of AI in informal (out of school) settings.

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