Further empirical studies that examine how higher

The gap

Evidence profile

Stated in the future work and limitations and cells research gap sections of the source papers, classified as general, spanning 2 journals.

Supporting evidence — 3 representative gaps

• Beyond Dashboards: A Systematic Literature Review of Learning Analytics, Business Intelligence, and Generative AI for Decision-Making in Universities (2026) · Journal of Computing Theories and Applications · doi

  Primarily focuses on learner-level instructional inter- action and pedagogy, with limited attention to insti- tutional decision infrastructures Emphasizes adoption and effectiveness, with com- paratively limited attention to operational decision processes, governance, and end-to-end analytical pipelines Commonly centers on learner-level analytics and predictive performance, with limited synthesis of de- ployment architectures and institutional decision in- frastructures Frequently treats BI/BDA as organizational report- ing or managerial capability rather than as an inte- grated computational decision stack Primarily catalogues applications and ethical issues, with limited integration of BI architectures, govern- ance mechanisms, validation practices, and complete decision pipelines As summarized in Table 1, prior review streams have contributed substantially to under- standing analytics, AI, and educational technologies in higher education. Nevertheless, these studies remain largely organized around separate emphases, including learner-facing interac- tion, technology adoption, dashboard utilization, organizational readiness, or AI applications. This separation limits understanding of how data ecosystems, BI infrastructures, AI models, governance mechanisms, GenAI interfaces, and HITL routines collectively function as insti- tutional decision-support infrastructures. The present review addresses this gap by integrating LA, BI, AI, GenAI, governance, and HITL mechanisms through a computing-oriented deci- sion-intelligence perspective. Journal of Computing Theories and Applications 2026 (May), vol. 3, no. 4, Purwanto, et al. 601 2.2 Key Constructs and Operational Definitions Because this review integrates concepts from education, information systems, data sci- ence, and AI governance, Table 2 defines the principal constructs used throughout the man- uscript and grounds them in representative literature. The table clarifies both the operational meaning of each construct and the computing-oriented analytical boundary adopted in this review. Table 2. Key constructs and operational definitions used in this review.

  generalstated in future workevidence 5/5
  Keywords: decision review limited operational governance learner infrastructures applications mechanisms computing constructs primarily level attention insti
• Guidelines for Designing AI Technologies to Support Adult Learning (2026) · doi

  Our guidelines should be interpreted with respect to the context from which we collected data, that is, adult learning environments. While these tools represent a wide variety of functions, they may not capture the full spectrum of educational technologies in use across different institutions and contexts. Though we have demon- strated the practicality and applicability of the guidelines to produce meaningful change in AI technologies for adult learners, it is unclear if and how the guidelines can be applied beyond adult and online learning contexts. Some of the guidelines may generalize across different age groups and modalities, while others, such as scaffold- ing social competencies and supporting career-oriented goals, may be specific to adult learners balancing professional, personal, and educational responsibilities. In addition, our analysis relied on thematic coding and heuris- tic evaluation. Although we used reflexive thematic analysis and consensus-building sessions to strengthen reliability, the judgments necessarily reflect researcher and coder interpretation. While the guidelines capture stakeholder concerns and priorities, further em- pirical validation is needed to assess their impact on learning, en- gagement, and adoption in practice. Future work should extend these guidelines through empirical validation, examining how these guidelines relate to learning out- comes, motivation, and technology use in authentic instructional settings. Cross-institutional studies, such as workforce training, community colleges, or international contexts, could test whether the guidelines hold in settings with different learner demographics, institutional structures, and resource constraints. Such work would help determine which guidelines are broadly applicable and which are more context-specific.

  generalstated in limitationsevidence 5/5
  Keywords: guidelines adult learning different contexts context capture educational technologies across learners specific thematic validation settings
• Learning Design and Learning Analytics to Improve Higher Education: A Systematic Literature Review (2026) · The Electronic Journal of e-Learning · doi

  There is a need for further empirical studies that examine how higher education institutions can strategically use Learning Analytics in line with Learning Design to guide long-term planning and decision-making. - The majority of current research focuses on the advantages at the student and lecturer levels, with little attention paid to applications at the institutional level.

  generalstated in cells research gapevidence 5/5
  Keywords: there need further empirical studies examine higher education

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