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Multi-campus public higher education governance systems exist in 44 of the 50 U.S. states. They include all the largest and most influential public colleges and universities in the United States, educating fully 75 percent of the nation's public sector students. Their impact is enormous. And yet, they are largely neglected and as a tool for improvement are underutilized. Meanwhile, many states continue to struggle achieving their goals for higher education attainment, social and economic mobility, workforce development, equitable access and affordability, technological innovation, and human and environmental health. The dearth of scholarly research on these systems and their more effective use is explored in a forthcoming volume edited by the author. This paper extracts from that volume a set of specific ways in which systems can leverage their unique ability to use scale in service to their mission.

In this position paper, the authors lay out the imperative for equitable artificial intelligence (AI), highlighting the essential role of access-oriented institutions and calling on technology companies (both large and small), foundations, and local, state, and federal regulators to consult with the newly convened Complete College America Council on Equitable AI in Higher Education. Their belief is that equitable AI spans far beyond the risk of mis-trained data. How schools adopt or reject these tools, the priorities of AI vendors, access to resources that enable the use of these tools, and the systemic integration of historically underrepresented and underserved voices will shape whether technology amplifies privilege or fosters inclusivity. A three-fold framework is presented for understanding Equity in AI, considering not just the quality and unbiased nature of the data used to train generative AI machines but also who has access to conversations around policy and product, as well as which institutions have access to the resources and safety nets that enable innovation and experimentation in the field of AI. A disruptive new advisory council is proposed, the Complete College America Council on Equitable AI in Higher Education, composed of representatives from historically excluded institutions and, by extension, students. The authors urge policymakers, technologists, and funders to proactively consult the Council and disrupt systemic inequities by integrating AI into higher education rather than continue to perpetuate them. [This paper was created in partnership with T3 Advisory.]

Generative AI platforms like ChatGPT have exploded into our cultural awareness this year. Across post-secondary institutions, it was immediately apparent that faculty were eager to explore and discuss what this potentially disruptive technology might mean for them, their courses and their students. We wanted to create an opportunity for that discussion and to get a truer sense of initial faculty reactions than what sensational media headlines were offering. This working paper outlines the results of a facilitated online forum, open to faculty and staff from two institutions in the Lower Mainland of British Columbia in January 2023. Our session invited participants to test ChatGPT, reflecting on its strengths and limitations, and then talk through the potential impacts on instructors, our students, and post-secondary education in general of different approaches: ignore it, fight it, and embrace it. Analysis of participant contributions to polls, group discussions and a highly active chat space provide a snapshot of how faculty and staff were feeling and what they were doing in response to ChatGPT and other generative AI platforms. While the data seems to indicate a relatively optimistic take at this early point in the AI revolution, excerpts from discussions and debates do indicate a range of emotions and reactions--a range that will likely only continue to widen with the continuing release of ever more capable AI.

This paper summarises exploratory analysis undertaken to evaluate the effectiveness of using machine learning approaches to calculate projected completion rates for vocational education and training (VET) programs, and compares this with the current approach used at the National Centre for Vocational Education Research (NCVER) -- Markov chains methodology. While the Markov chains methodology currently used by NCVER has demonstrated that it is reliable, with predictions aligning well with the actual rates of completion for historical estimates, it has not been reviewed for some time and it does have some limitations. The evaluation of machine learning techniques for predicting VET program completion rates was undertaken to overcome some of these limitations and with a view to improving our current predictions. This report includes: (1) an overview of the methodologies: Markov chains and two machine learning algorithms that were applied to predict completion rates for VET programs (XGBoost and CatBoost); (2) a comparison of the accuracy of the predictions generated by both methodologies; and (3) an evaluation of the relative strengths and limitations of both methodologies.

Calls are intensifying for workforce reskilling and a re-engineering of education and training to meet the demands of the future. Current policy in England focuses on technical, digital and green economy skills, underpinned by strong literacy and numeracy and a knowledge-rich school curriculum. National Foundation for Educational Research's Nuffield-funded research study, "The Skills Imperative 2035: Essential skills for tomorrow's workforce" investigates: (1) which essential employment skills will be most needed in 2035; (2) what will their likely supply be and where will the gaps be; (3) which occupations and workers are most at risk of not having these skills; (4) which skills will affected workers need to develop to transition into new employment opportunities; and (5) the role of educators and employers in helping to prepare young people and workers for the future labour market. This first report, a review drawing on a wide-ranging and growing evidence base, sets the scene for the wider research study by bringing together what the literature suggests about: (1) what the world of work will look like in 2035; and (2) which essential employment skills will be in demand and how what should be done to prepare.

Digital technologies can be used to support the inclusion of diverse student groups in education in a number of ways including enhancing accessibility of educational content, increasing personalisation and providing distance learning opportunities, as was the case during the COVID-19 pandemic. However, persistent digital inequalities can undermine digital equity and inclusion and equity and inclusion in education generally, particularly for the most disadvantaged students. This paper explores the themes of digital equity and inclusion, and maps some of the policies and practices adopted in OECD countries for the equitable and inclusive use of digital tools in education. It highlights the importance of inclusive design and implementation of digital technologies, as well as the need for education systems to focus on capacity building such as teacher training, as well as adequate resourcing of digital tools. It discusses advantages and disadvantages of different approaches, and concludes by highlighting research and policy gaps.

The paper discusses the implications of recent advances in artificial intelligence for knowledge workers, focusing on possible complementarities and substitution between machine translation tools and language professionals. The emergence of machine translation tools could enhance social welfare through enhanced opportunities for inter-language communication but also create new threats because of persisting low levels of accuracy and quality in the translation output. The paper uses data on online job vacancies to map the evolution of the demand for language professionals between 2015 and 2019 in 10 countries and illustrates the set of skills that are considered important by employers seeking to hire language professionals through job vacancies posted on line.

This Policy Paper aims at analysing the most important aspects of Public Responsibility, Financing and Governance of Higher Educations while seeking to formulate a students perspective on the state of play within the European Higher Education Area (EHEA). In doing so it touches upon the very foundation of how and in which socio-political environment educational systems and higher education institutions work nowadays. The European Students' Union (ESU) believes that open access to all levels of education is the cornerstone of a socially, culturally and democratically inclusive society, and a prerequisite for individual and societal development and well-being. ESU sees higher education as a human right, which is guaranteed in the Universal Declaration of Human Rights and the International Covenant on Economic, Social and Cultural Rights. How education is seen in society, how it is funded and how it is governed are tightly interlinked areas. This policy paper focuses on: (1) Public responsibility of higher education (fundamental values; institutional autonomy and academic freedom; academic integrity; intellectual property; education for sustainable development; human rights and democratic citizenship education; digitalization, artificial intelligence, learning analytics and privacy; commodification; partnerships between higher education institutions and industry; internships; and internationalisation and international trade); (2) Financing of higher education (financing of higher education; the funding gap; optimisation of funding of higher education institutions; performance based funding; and education free of tuition fees); and (3) Governance of higher education (students participation; working conditions of academic staff; committees and ombudsmans and leadership, intersectionality and training). [For the 2016 version, see ED587168.]

We are living in an era of artificial intelligence (AI). There is wide discussion about and experimentation with the impact of AI on education/higher education. In this paper, we give a discussion of how AI is evolving, explore the ways AI is changing education/higher education, give a concise account of the skills universities need to teach their students to prepare them for an AI world of work, and talk succinctly about the changing nature of jobs and the workforce.

Among community college leaders and others interested in reforms to improve student success, there is growing interest in adopting machine learning (ML) techniques to predict credential completion. However, ML algorithms are often complex and are not readily accessible to practitioners for whom a simpler set of near-term measures may serve as sufficient predictors. This study compares the out-of-sample predictive power of early momentum metrics (EMMs)--13 near-term success measures suggested by the literature--with that of metrics from ML-based models that employ approximately 500 predictors for community college credential completion. Using transcript data from approximately 50,000 students at more than 30 community colleges in two states, I find that the EMMs that were modeled by logistic regression accurately predict completion for approximately 80% of students. This classification performance is comparable to that of the ML-based models. The EMMs even outperform the ML-based models in probability estimation. These findings suggest that EMMs are useful predictors for credential completion and that the marginal gain from using an ML-based model over EMMs is small for credential completion prediction when additional predictors do not have strong rationales to be included in an ML-based model, no matter how large the number of those predictors may be.

Responsible Operations is intended to help chart library community engagement with data science, machine learning, and artificial intelligence (AI) and was developed in partnership with an advisory group and a landscape group comprised of more than 70 librarians and professionals from universities, libraries, museums, archives, and other organizations. This research agenda presents an interdependent set of technical, organizational, and social challenges to be addressed en route to library operationalization of data science, machine learning, and AI. Challenges are organized across seven areas of investigation: (1) Committing to Responsible Operations; (2) Description and Discovery; (3) Shared Methods and Data; (4) Machine-Actionable Collections; (5) Workforce Development; (6) Data Science Services; (7) Sustaining Interprofessional and Interdisciplinary Collaboration. Organizations can use Responsible Operations to make a case for addressing challenges, and the recommendations provide an excellent starting place for discussion and action.

Recent advances in computational linguistics and the social sciences have created new opportunities for the education research community to analyze relevant large-scale text data. However, the take-up of these advances in education research is still nascent. In this paper, we review the recent automated text methods relevant to educational processes and determinants. We discuss both lexical-based and supervised methods, which expand the scale of text that researchers can analyze, as well as unsupervised methods, which allow researchers to discover new themes in their data. To illustrate these methods, we analyze the text interactions from a field experiment in the discussion forums of online classes. Our application shows that respondents provide less assistance and discuss slightly different topics with the randomized female posters, but respond with similar levels of positive and negative sentiment. These results demonstrate that combining qualitative coding with machine learning techniques can provide for a rich understanding of text-based interactions.

Students often fail to write effective synthesis essays that compare multiple sources across common intersecting categories. Instead, they compose flawed essays that focus primarily on one source and then add a few ideas from other sources (patchwriting); report ideas from all sources in a disjointed fashion (tag-all writing); or draw from one source after another without comparison (separate-representation writing). Effective synthesis writing depends on three strategies: selecting important information from each source, arranging the selected information in a graphic organizer for easy comparison, and connecting information from the various sources in a comparative way. The authors report on an established teaching and learning system called SOAR (Select, Organize, Associate, and Regulate) and its newly investigated impact on synthesis writing in the two studies that they conducted. In the first study, students provided with SOAR supplements (a graphic organizer, association prompts, and a regulation checklist) composed essays that contained more information, better synthesis organization, and more intertextual relationships than did essays from students who were not using SOAR supplements. In the second study, SOAR-trained students composed better organized synthesis essays than students who used their own preferred strategies. Across studies, students found SOAR helpful for synthesis writing and reported that they would be likely to use SOAR for future writing assignments. The authors conclude with an example of how to teach students to use SOAR when they write.

This working paper identifies OECD countries' interests in digital innovation in education by analysing their policy papers on digital education. Many OECD countries have adopted a specific strategy on digital education, or integrated the topic in a generic strategy on digital innovation as such. The ideas that are expressed in the strategies differ greatly; some are work in progress, others contain bold envisions of the future. There is a high awareness among OECD countries of the benefits of digitalisation, and the role of government to support digital innovation in education. This paper covers and documents countries' policy focus before the 2020 coronavirus crisis.

This paper was written to support the G20 artificial intelligence (AI) dialogue. With the rise of artificial intelligence (AI), education faces two challenges: reaping the benefits of AI to improve education processes, both in the classroom and at the system level; and preparing students for new skillsets for increasingly automated economies and societies. AI applications are often still nascent, but there are many examples of promising uses that foreshadow how AI might transform education. With regard to the classroom, this paper highlights how AI can accelerate personalised learning, the support of students with special needs. At the system level, promising uses include predictive analysis to reduce dropout, and assessing new skillsets. A new demand for complex skills that are less easy to automate (e.g. higher cognitive skills like creativity and critical thinking) is also the consequence of AI and digitalisation. Reaching the full potential of AI requires that stakeholders trust not only the technology, but also its use by humans. This raises new policy challenges around "trustworthy AI", encompassing the privacy and security of data, but also possible wrongful uses of data leading to biases against individuals or groups.

Researchers often include covariates when they analyze the results of randomized controlled trials (RCTs), valuing the increased precision of the estimates over the potential of inducing small-sample bias when doing so. In this paper, we develop a sufficient condition which ensures that the inclusion of covariates does not induce small-sample bias in the effect estimates. Using this result as a building block, we develop a novel approach that uses machine learning techniques to reduce the variance of the average treatment effect estimates while guaranteeing that the effect estimates remain unbiased. The framework also highlights how researchers can use data from outside the study sample to improve the precision of the treatment effect estimate by using the auxiliary data to better model the relationship between the covariates and the outcomes. We conclude with a simulation, which highlights the value of using the proposed approach.

The five papers in this collection discuss various aspects of the Electronic Learning-Package Factory (ELF) project at the University of Bradford in England. In the first paper, "Adoption of CAL in Higher Education: A Cooperative Approach to Research, Development and Implementation," Philip Barker considers the opportunities for collaborative implementation of simple user-friendly software tools in computer assisted learning (CAL) in higher education. In the second paper, "The Computerisation of a UK University: The Bradford Experience," Tom Stonier, Stephen J. Fallows, and Andrew Radtke examine the background to the computerization project and the rationales behind providing networking facilities to the entire campus and encouraging ownership of microcomputers. In the third paper, "The Development of ELFsoft: A User-Friendly CAL Authoring System," T. R. King and S. J. Fallows examine a form-based design system called the ELF Starter Pack, which can be used by novice computer users to generate simple but sophisticated software. The features and technical requirements of the ELF Starter Pack are reviewed by T. R. King and S. J. Fallows in the fourth paper, "ELFSoft: A Simple But Effective CAL Authoring System." (In the fifth paper, "A Study of Attitudes towards Computerisation of the University of Bradford," A. L. Radtke and T. Stonier describe a survey of 190 students and 321 faculty designed to ascertain their opinions on computerization of the university, as well as to collect data on their current and projected usage of microcomputers. (DB)

Recent advances in artificial intelligence and the cognitive sciences have made it possible to develop successful intelligent computer-aided instructional systems for technical and scientific training. In addition, computer-aided design (CAD) environments that support the rapid development of such computer-based instruction have also been recently developed. The researchers tailored a particular CAD system for instruction, called the Instructional Design Environment (IDE), for use in vocational training. This project brought together a consulting team that included a successful instructor in business education, cognitive scientists, workers in teacher education, and the IDE development team. The main goals of the project were to develop an instructional design methodology that teaches software use in the context of solving realistic problems and to extend the IDE to support this methodology, which is grounded in cognitive science research and is called example-based minimalist design (EBMD). It was found that the use of IDE has several side effects: (1) IDE encourages a greater depth of analysis and planning; (2) the semiformal representation language used in IDE shapes the design process and the manner in which the designer thinks about instruction; (3) the analyses and specifications developed in IDE provide an explicit design rationale for each product; and (4) designs and design rationales developed in IDE can be easily modified and reused thus standardizing instructional development and promoting dissemination of successful design methodologies. (Contains 11 references.) (ALF)

A methodological framework is presented for representing tradeoffs among alternative combinations of training and aiding for personnel in complex situations. In general, more highly trained people need less aid, and those with less training need more aid. Balancing training and aiding to accomplish the objectives of the system in a cost effective way is the concern. A wide variety of methods, tools, and models is reviewed. These approaches are evaluated in terms of their advantages and disadvantages when used to analyze training/aiding tradeoffs. The use of the proposed framework and its component methods, tools, and models is illustrated by an analysis of a realistically complex example involving the design of a head-up display for use by truck drivers in long-haul transport. Results demonstrate that the tradeoff issue can be involved in other than an ad hoc manner. Research needed in predictive models, learning processes, and intelligent systems is reviewed. Four tables and nine figures illustrate the discussion. (SLD)

This document contains abstracts of 29 research projects in progress in Great Britain divided into six sections: (1) the current phase of Information Technology in Education Research (InTER) programs on groupwork with computers, tools for exploratory learning, conceptual change in science, and bubble dialogue as an ethnographic research tool; (2) projects supported by other agencies on computer-aided learning in music, computer testing of musical ability, effects of gender and interaction in computer-based learning, computer models of reading and spelling, computer-supported collaborative learning in physics, a computer-based program for mathematics teachers, the gap between arithmetical and algebraic thinking, computers in the secondary school curriculum, and information technology and training; (3) Economic and Social Research Council Linked Research Students projects on a knowledge-based approach to question answering in online systems and the design of knowledge-based advisers for learning; (4) Training Agency projects on supporting technology across the curriculum, whole school development in information technology, computer-based modelling, information technology-based open learning, authoring environments for simulation, and the DISTIL survey; (5) Developing European Learning through Technological Advance (DELTA) projects on a knowledge-based authoring facility, European cooperation in technology-based education, and student model acquisition in a natural laboratory; and (6) updates of InTER projects on the design of the Writer's Assistant and characteristics of human search procedures. (MES)

In 2023, ChatGPT emerged as a transformative force in education, igniting widespread interest across academia. This paper rigorously investigates ChatGPT's impact on higher education using a mixed-methods approach, comparing its (ChatGPT) performance with real students' work in undergraduate assignments. Key findings reveal ChatGPT consistently outperformed students, achieving the top mark in 8 of 10 classes, indicating its potential within higher education. Instructors accurately identified 50% of AI-generated assessments; however, with new AI tools from TurnItIn, this jumped to a detection rate of 92%. Ethical concerns arose, with 65% of surveyed undergraduates admitting ChatGPT use to complete academic work. This research significantly contributes to the discourse on educational technology, shedding light on the implications of AI integration in academia.

This research study explores the potential of a pedagogical model of self-regulated learning supported with Artificial Intelligence (AI) chatbots to enhance self-expression and reflective writing skills for novice EFL student teachers at Faculty of Education, Assiut University. The study adopted a pre-post quasi-experimental design, that starts with the identification of the necessary self-expression and reflective writing skills for the target participants (50 fresh EFL student teachers at Assiut University who were purposively selected using a screening questionnaire based on their basic IT literacy skills). A pre-test was administered to assess their initial skill levels in self-expression and reflective writing. Then, an intervention was implemented in the form of a pedagogical model designed around the principles of self-regulated learning and situated language learning, which guided the use of AI chatbots (Bing, ChatGPT, and Google Bard). This model was initially piloted on a small sample (n = 10) of EFL student teachers to check validity and reliability and then experimented with the research participants for 8 weeks during the first semester of the academic year 2023/24. Following the intervention, a post-test was conducted to measure the participants' levels of self-expression and reflective writing skills after being exposed to the interventional model, aiming to identify any improvements gained from the intervention. The results indicated a positive effect with noticeable enhancements in the EFL student teachers' skills. This suggests the potential effectiveness of the model in fostering self-expression and reflective writing skills and developing EFL student teachers' general language proficiency and IT literacy. [This paper was published in "Academic Journal of Faculty of Education, Assiut University" v40 n9 p1-50 2024.]

The article delves into various policy papers recently published by the UK Government and parliamentary libraries, as well as insights from think tanks and other organizations. It covers topics such as T Level education plans, the integration of AI (artificial intelligence) in education regulation, findings from the National Behaviour Survey, and discussions on child poverty statistics and the UK's policy response.