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Artificial intelligence (AI) refers to a type of algorithms or computerized systems that resemble human mental processes of decision-making. This position paper looks beyond the sensational hyperbole of AI in teaching and learning. Instead, this paper aims to explore the role of AI in educational leadership.

To explore the role of AI in educational leadership, I synthesized the literature that intersects AI, decision-making, and educational leadership from multiple disciplines such as computer science, educational leadership, administrative science, judgment and decision-making and neuroscience. Grounded in the intellectual interrelationships between AI and educational leadership since the 1950s, this paper starts with conceptualizing decision-making, including both individual decision-making and organizational decision-making, as the foundation of educational leadership. Next, I elaborated on the symbiotic role of human-AI decision-making.

With its efficiency in collecting, processing, analyzing data and providing real-time or near real-time results, AI can bring in analytical efficiency to assist educational leaders in making data-driven, evidence-informed decisions. However, AI-assisted data-driven decision-making may run against value-based moral decision-making. Taken together, both leaders' individual decision-making and organizational decision-making are best handled by using a blend of data-driven, evidence-informed decision-making and value-based moral decision-making. AI can function as an extended brain in making data-driven, evidence-informed decisions. The shortcomings of AI-assisted data-driven decision-making can be overcome by human judgment guided by moral values.

The paper concludes with two recommendations for educational leadership practitioners' decision-making and future scholarly inquiry: keeping a watchful eye on biases and minding ethically-compromised decisions.

This paper brings together two fields of educational leadership and AI that have been growing up together since the 1950s and mostly growing apart till the late 2010s. To explore the role of AI in educational leadership, this paper starts with the foundation of leadership—decision-making, both leaders' individual decisions and collective organizational decisions. The paper then synthesizes the literature that intersects AI, decision-making and educational leadership from multiple disciplines to delineate the role of AI in educational leadership.

Program budgeting and marginal analysis (PBMA) is a priority setting approach that assists decision makers with allocating resources. Previous PBMA work establishes its efficacy and indicates that contextual factors complicate priority setting, which can hamper PBMA effectiveness. The purpose of this paper is to gain qualitative insight into PBMA effectiveness.

A Canadian case study of PBMA implementation. Data consist of decision-maker interviews pre (n=20), post year-1 (n=12) and post year-2 (n=9) of PBMA to examine perceptions of baseline priority setting practice vis-à-vis desired practice, and perceptions of PBMA usability and acceptability.

Fit emerged as a key theme in determining PBMA effectiveness. Fit herein refers to being of suitable quality and form to meet the intended purposes and needs of the end-users, and includes desirability, acceptability, and usability dimensions. Results confirm decision-maker desire for rational approaches like PBMA. However, most participants indicated that the timing of the exercise and the form in which PBMA was applied were not well-suited for this case study. Participant acceptance of and buy-in to PBMA changed during the study: a leadership change, limited organizational commitment, and concerns with organizational capacity were key barriers to PBMA adoption and thereby effectiveness.

These findings suggest that a potential way-forward includes adding a contextual readiness/capacity assessment stage to PBMA, recognizing organizational complexity, and considering incremental adoption of PBMA’s approach.

These insights help us to better understand and work with priority setting conditions to advance evidence-informed decision making.

The purpose of this article is to discuss approaches to sustainable decision-making for integrating emerging educational technologies in library instruction while supporting evidence-based practice (EBP).

This article highlights recent trends in emerging educational technologies and EBP and details a model for supporting evidence informed decision-making. This viewpoint article draws on an analysis of recent literature, as well as experience from professional practice.

Authors discuss the need for sustainable decision-making that addresses a perceived lack of evidence surrounding emerging technologies, a dilemma that many library educators and practitioner-researchers will have faced in their own library instruction. To support the evidence-informed selection and integration of emerging educational technologies, a two-pronged model is presented, beginning with an articulation of pedagogical aims, alignment of technological affordances to these aims and support of this alignment via hard evidence available in the research literature, as well as soft evidence found in the environmental scan.

This article provides an outline and synthesis of key issues of relevance to library practitioners working within a challenging and ever-changing landscape of technologies available for learning and instruction. The proposed approach aims to create a sustainable model for addressing problems of evidence and will benefit academic librarians considering emerging educational technologies in their own pedagogy, as well as those who support the pedagogy of others.

One of the major issues faced by academic health science centers (AHSCs) is the need for mechanisms to foster the integration of research, clinical, and educational activities to achieve the vision of evidence-informed decision making (EIDM) and optimal client care. The paper aims to discuss this issue.

This paper synthesizes literature on organizational learning and collaboration, evidence-informed organizational decision making, and learning-based organizations to derive insights concerning the nature of effective workplace learning in AHSCs.

An evidence-informed model of collaborative workplace learning is proposed to aid the alignment of research, clinical, and educational functions in AHSCs. The model articulates relationships among AHSC academic functions and sub-functions, cross-functional activities, and collaborative learning processes, emphasizing the importance of cross-functional activities in enhancing collaborative learning processes and optimizing EIDM and client care. Cross-functional activities involving clinicians, researchers, and educators are hypothesized to be a primary vehicle for integration, supported by a learning-oriented workplace culture. These activities are distinct from interprofessional teams, which are clinical in nature. Four collaborative learning processes are specified that are enhanced in cross-functional activities or teamwork: co-constructing meaning, co-learning, co-producing knowledge, and co-using knowledge.

The model provides an aspirational vision and insight into the importance of cross-functional activities in enhancing workplace learning. The paper discusses the conceptual and empirical basis to the model, its contributions and limitations, and implications for AHSCs.

The model’s potential utility for health care is discussed, with implications for organizational culture and the promotion of cross-functional activities.

The purpose of this paper is to better understand facilitators’ perceived role and influence on a policy dialogue’s (PD) process and impact. PDs enable interactions between policy makers, researchers and other stakeholders – one of the factors associated with promoting evidence-informed policy making.

This is an exploratory study based on semi-structured interviews with ten key informants from sub-Saharan Africa, the Middle East, North and South America. Participants were purposefully sampled based on their experience in facilitating or observing PDs organized by the WHO’s Evidence-informed Policy Network. Data were analyzed using a constant comparative method.

A successful PD relies on a structured process used to catalyze impact. Facilitators contribute to a successful PD through their facilitation skills, for example, helping to get to an informed judgment; knowledge, for example, about the health system; attitudes, for example, valuing the PD process over its outcomes; and personal attributes, for example, credibility. Facilitators’ involvement in preparatory and follow-up actions are equally paramount for a PD’s success. Challenges in implementing PDs can be prevented/attenuated, for example, through stakeholder analysis to identify suitable PD participants, and anticipate power constellations or potential conflicts.

Research should focus on the overall process of a PD – especially on preparation and follow-up activities and their influence on a PD’s success.

Informed by harnessing practical experiences, this paper outlines facilitators’ skills, attributes, attitudes, knowledge and how these can be used to influence a PD’s success.

This paper aims to increase the transparency of information in official anti-money laundering rating data to assist evidence-informed decision-making in compliance, policy-making and research.

This paper converts anti-money laundering rating data into information-rich visualisations, reintroduces a comparison methodology and ranks all anti-money laundering regimes evaluated to date.

Official anti-money laundering ratings as currently structured and presented offer surprisingly little policy-relevant information. Persistent failure to transform available data into information for knowledge and insight suggests that the risk has been realised that impressionistic judgments or politicised interests drive the policy agenda at least as much as objective evidence or substantive economic and social goals.

Any reluctance to generate policy-relevant information from the industry’s primary data set or disinclination to engage constructively with a growing body of independent critical policy effectiveness evidence calls into question whether implementing anti-money laundering controls with some prospect of achieving substantial societal benefits, or perpetuating the current system, prevails.

With a dearth of scholarship at the intersection of money laundering and policy effectiveness scholarship and practice, this paper combines elements of these disciplines and examines anti-money laundering effectiveness from a different viewpoint. Rather than seeking to measure money laundering or estimate the proportion of criminal proceeds successfully intercepted, this paper draws directly from the anti-money laundering industry’s own “main” data set.

This paper aims to describe methods and models designed to build a comprehensive, integrative framework to guide the research to policy and practice cycle in health care.

Current models of science are summarised, identifying specific challenges they create for knowledge to action (KTA). Alternative models for KTA are outlined to illustrate how researchers and decision makers can work together to fit the KTA model to specific problems and contexts. The Canadian experience with the evolving paradigm shift is described, along with recent initiatives to develop platforms and tools that support the new thinking. Recent projects to develop and refine methods for embedded research are described. The paper concludes with a summary of lessons learned and recommendations that will move the KTA field towards an integrated science.

Conceptual models for KTA are advancing, benefiting from advances in team science, development of logic models that address the realities of complex adaptive systems, and new methods to more rapidly deliver knowledge syntheses more useful to decision and policy makers.

KTA is more likely when co‐produced by researchers, practitioners, and policy makers. Closer collaboration requires shifts in thinking about the ways we work, capacity development, and greater learning from practice.

More powerful ways of thinking about the complexities of knowledge to action are provided, along with examples of tools and priorities drawn from systems thinking.

The purpose of this paper is to describe how an Academic Health Science Centre, providing pediatric rehabilitation services, research, and education, developed a Centres for Leadership (CfL) initiative to integrate its academic functions and embrace the goal of being a learning organization.

Historical documents, tracked output information, and staff members’ insights were used to describe the ten-year evolution of the initiative, its benefits, and transformational learnings for the organization.

The evolutions concerned development of a series of CfLs, and changes over time in leadership and management structure, as well as in operations and targeted activities. Benefits included enhanced clinician engagement in research, practice-based research, and impacts on clinical practice. Transformational learnings concerned the importance of supporting stakeholder engagement, fostering a spirit of inquiry, and fostering leaderful practice. These learnings contributed to three related emergent outcomes reflecting “way stations” on the journey to enhanced evidence-informed decision making and clinical excellence: enhancements in authentic partnerships, greater innovation capacity, and greater understanding and actualization of leadership values.

Practical information is provided for other organizations interested in understanding how this initiative evolved, its tangible value, and its wider benefits for organizational collaboration, innovation, and leadership values. Challenges encountered and main messages for other organizations are also considered.

A strategy map is used to present the structures, processes, and outcomes arising from the initiative, with the goal of informing the operations of other organizations desiring to be learning organizations.