Generative AI-first Maths Support at a Regional Australian University

AI as a Study Partner in Mathematics Education: Evidence from a Multi-Program University Survey

The findings indicate that students integrate AI tools primarily as complementary learning aids rather than replacements for traditional materials, and highlights the growing importance of evaluating not only usage frequency but also perceived reliability and pedagogical value.

The Role of Artificial Intelligence in Education: Opportunities, Challenges, and Implications for Formal and Non-Formal Learning

AI INTEGRATION IN SECONDARY SCHOOLS: EFFECTS ON STUDENT ENGAGEMENT AND ACHIEVEMENT

Across the world, secondary schools are experimenting with artificial intelligence (AI) to personalize instruction, automate feedback, and augment teachers’ capacity. Early evidence suggests AI can boost certain forms of engagement and achievement especially through intelligent tutoring systems (ITS), adaptive practice, and teacher-facing assistants. Yet risks remain around shallow learning, inequity, and student data protection. This article synthesizes research on the impact of AI on secondary students’ engagement (behavioral, emotional, cognitive) and achievement (course grades, standardized tests, mastery), and also contrasts traditional ITS findings with new evidence on generative AI. I propose a practical implementation blueprint covering pedagogy, staffing, procurement, safety, and evaluation, along with a responsible use and governance checklist aligned to recent policy guidance (e.g., UNESCO) [1] and regulation (e.g., EU AI Act)[2]. Overall, we can conclude that schools can realize gains if they couple AI with clear learning goals, teacher capacity-building, and robust measurement plans.

Generative AI Availability, Grades, and Student Satisfaction at a Large University

It is found that there is no significant differential effect of GenAI availability on grades overall or among previously lower-performing students, and the findings temper concerns that GenAI inflates grades and reduces students's satisfaction.

When AI is Not AI

Empirical research into these higher education chatbots has largely focused on how chatbots are used in two domains: 1) helping students through the enrollment management (ex: admissions, financial aid, housing) process and 2) providing instructor support to enhance student learning in a curricular context. This study reports on structured human-computer interactions with higher education chatbots to explore whether these chatbots were programmed to provide financial counseling to college students. During our structured interactions with chatbots, we learned that many systems marked as AI chatbots fell short of adaptive, generative capabilities which are the essence of AI systems (Regulations (EU) 2024/1689, Art. 3(1)). Specifically, we identified five points along the capability spectrum of generating responses: 1) chatbots were often “offline” during business hours, rendering them non-functional; 2) chatbots were fillable forms, thus functioning like emails; 3) chatbots were decision trees and not large language models, thus functioning like search engines and lacking adaptive response generation; 4) chatbots were actually chat applications staffed by human agents without disclosure of their non-automation; and 5) artificial intelligence chatbots were not overly intelligent in its responses to our queries. We address the implications of these findings for both research and communication ethics, sustainability, and institutional efficiency.

The Role of Artificial Intelligence in Education: Opportunities, Challenges, and Implications for Formal and Non-Formal Learning

Artificial intelligence (AI) has moved rapidly from a specialised research topic into a routine presence in classrooms, training programmes and self-directed study, a shift accelerated by the public release of generative tools. This paper examines the role of AI across both formal and non-formal education, with the objective of clarifying where the technology adds genuine pedagogical value, where it introduces risk, and what conditions are needed for responsible adoption. The study adopts a narrative and critical review of peer-reviewed literature, foundational scholarship and policy guidance published mainly between 2016 and 2024. Sources were identified through academic databases and synthesised thematically around application domains, reported benefits and recurring concerns, rather than through statistical meta-analysis. Four broad application areas emerge: personalised and adaptive learning; intelligent tutoring and automated assessment; generative AI for content creation and dialogue; and AI-supported non-formal and lifelong learning. Reported benefits include wider access, individualised pacing and reduced routine workload for educators. Persistent concerns cluster around academic integrity, algorithmic bias, data privacy, the digital divide, and a possible erosion of independent reasoning when learners over-rely on automated output. In conclusion, AI is best understood as an amplifier of pedagogy, rather than a replacement for teachers or human judgment. Its benefits are conditional on AI literacy, transparent governance, and equitable access. Non-formal settings, being flexible and learner-driven, are particularly well placed to exploit AI, provided the same ethical safeguards apply.