FEU TECH Educational Innovation and Technology Hub

𝐌𝐞𝐞𝐭 𝐭𝐡𝐞 𝐠𝐮𝐞𝐬𝐭𝐬 𝐨𝐟 𝐄𝐝𝐢, 𝐃'𝐓𝐚𝐥𝐤𝐬 𝐄𝐩𝐢𝐬𝐨𝐝𝐞 𝟓!

In honor of Guidance and Counseling Month, we are sitting down with the incredible team from the FEU Tech Guidance and Counseling Office (GCO)! 💚💛

From navigating academic stress to managing personal hurdles, they’re here to remind every Tamaraw that the GCO is a safe space. 🌱

🎖 𝐍𝐄𝐖 𝐏𝐔𝐁𝐋𝐈𝐂𝐀𝐓𝐈𝐎𝐍 𝐀𝐋𝐄𝐑𝐓 🎖

𝘛𝘐𝘛𝘓𝘌: Transparency, Ethical Framing, and User Agency as Determinants of Trust in AI-Mediated Assessment: Informing the Design of Trustworthy Systems

𝘈𝘜𝘛𝘏𝘖𝘙:

🇵🇭 𝗠𝗮𝗻𝘂𝗲𝗹 𝗕. 𝗚𝗮𝗿𝗰𝗶𝗮
𝗙𝗘𝗨 𝗜𝗻𝘀𝘁𝗶𝘁𝘂𝘁𝗲 𝗼𝗳 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆, 𝗣𝗵𝗶𝗹𝗶𝗽𝗽𝗶𝗻𝗲𝘀

𝘈𝘉𝘚𝘛𝘙𝘈𝘊𝘛:

As artificial intelligence (AI) systems assume greater responsibility in educational assessment, questions surrounding fairness, transparency, and trust have become central to their ethical and pedagogical legitimacy. Yet, little empirical work has examined how specific design features shape students’ trust in AI-driven assessment, particularly in contexts where algorithmic decisions carry meaningful academic consequences. This study examines how transparency, ethical framing, and user agency influence students’ trust in an AI-based assessment platform. Using a 2 × 2 × 2 between-subjects experimental design with 240 undergraduate participants, the study isolates the main and interaction effects of these variables on trust, perceived fairness, perceived control, and adoption intention. Findings indicate that transparency is the most influential predictor of trust, while user agency functions as a compensatory mechanism in low-transparency conditions. Ethical framing, although theoretically salient, showed limited impact once users interacted with the system directly and shifted their attention toward the more concrete procedural cues embedded in the interface. A significant interaction between transparency and agency underscores the importance of aligning epistemic clarity with procedural control to foster behavioral commitment. These results support a multidimensional model of trust that incorporates emotional security, procedural justice, and behavioral intent. Overall, the study underscores that trust in AI assessment is not a byproduct of system accuracy alone but a reflection of students’ perceived legitimacy of the evaluative process.

𝘗𝘜𝘉𝘓𝘐𝘚𝘏𝘌𝘋 𝘐𝘕: Evaluation Review

𝘗𝘜𝘉𝘓𝘐𝘊𝘈𝘛𝘐𝘖𝘕 𝘋𝘈𝘛𝘌: May 9, 2026

𝘗𝘜𝘉𝘓𝘐𝘚𝘏𝘌𝘙: SAGE

𝘋𝘖𝘐: 10.1177/0193841X261451124

🎖 𝐍𝐄𝐖 𝐏𝐔𝐁𝐋𝐈𝐂𝐀𝐓𝐈𝐎𝐍 𝐀𝐋𝐄𝐑𝐓 🎖

𝘛𝘐𝘛𝘓𝘌: A Framework for Generative AI Policy and Guidelines in K-12 Education

𝘈𝘜𝘛𝘏𝘖𝘙𝘚:

🇺🇸 Helen Cropton
Old Dominion University, USA

🇺🇸 Diane Burke
Old Dominion University, USA

🇹🇷 Aras Bozkurt
Anadolu University, Turkey

🇺🇸 Christine Nickel
Old Dominion University, USA

🇫🇷 Fengchun Miao
UNESCO, France

🇦🇺 Mark Pegrum
University of Western Australia, Australia

🇺🇸 John Curry
Idaho State University, USA

🇳🇿 David Parsons
academyeX, New Zealand

🇶🇦 Adam Edmett
British Council, Qatar

🇺🇸 LeeAnn Lindsey
Northern Arizona University, USA

🇵🇭 𝗠𝗮𝗻𝘂𝗲𝗹 𝗕. 𝗚𝗮𝗿𝗰𝗶𝗮
𝗙𝗘𝗨 𝗜𝗻𝘀𝘁𝗶𝘁𝘂𝘁𝗲 𝗼𝗳 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆, 𝗣𝗵𝗶𝗹𝗶𝗽𝗽𝗶𝗻𝗲𝘀

🇿🇦 Agnes Chigona
Cape Peninsula University of Technology, South Africa

🇺🇸 Curtis J. Bonk
Indiana University, USA

🇪🇸 Francisco Bellas
Universidade da Coruña, Spain

🇲🇦 Mourad Benali
Regional Center for Education and Training Professions in the Oriental Region, Morocco

🇺🇸 Pat Yongpradit
Code.org, USA

🇦🇺 Agnieszka Palalas
Athabasca University, Australia

🇧🇷 Andreia de Bem Machado
Federal University of Santa Catarina, Brazil

🇰🇷 Hasan Tinmaz
Woosong University, South Korea

🇺🇸 Jinhee Kim
Old Dominion University, USA

🇸🇪 Johanna Velander
Linnaeus University, Sweden

🇨🇳 Junhong Xiao
Open University of Shantou, China

🇬🇾 Lenandlar Singh
University of Guyana, Guyana

🇺🇸 Mohan Yang
Texas A&M University, USA

🇲🇾 Mohd Ali Bin Samsudin
Universiti Sains Malaysia, Malaysia

🇹🇼 Sean Yu
Independent Researcher, Taiwan

𝘈𝘉𝘚𝘛𝘙𝘈𝘊𝘛:

The rapid emergence of generative artificial intelligence (GenAI) has introduced both opportunities and challenges for education systems worldwide. Educational stakeholders are grappling with fundamental questions of how to guide students on whether and when, and in what ways, they should use GenAI. In this study, a framework was developed to guide K–12 policies and guidelines on the use of GenAI. Using the Delphi technique and collective writing, expert perspectives were gathered from participants across 20 countries and six continents. The analysis identified eight key topic areas for K–12 GenAI policy and guideline development: (1) data privacy and security, (2) ethical and responsible use, (3) equitable access, (4) academic integrity, (5) human oversight, (6) GenAI literacy, (7) curriculum integration, and (8) governance and review. A complementary six-part framework was also constructed to support policy relevance and currency through multi-stakeholder governance, continuous review, ongoing training, awareness of external developments, outcome monitoring, and transparent communication. Together, these frameworks advance the scholarly and practical understanding of how GenAI policies can be designed and maintained in schools.

𝘗𝘜𝘉𝘓𝘐𝘚𝘏𝘌𝘋 𝘐𝘕: Journal of Research on Technology in Education

𝘗𝘜𝘉𝘓𝘐𝘊𝘈𝘛𝘐𝘖𝘕 𝘋𝘈𝘛𝘌: May 1, 2026

𝘗𝘜𝘉𝘓𝘐𝘚𝘏𝘌𝘙: Taylor & Francis

𝘋𝘖𝘐: 10.1080/15391523.2026.2661641

𝐘𝐨𝐮'𝐯𝐞 𝐠𝐨𝐭 𝐚 𝐟𝐫𝐢𝐞𝐧𝐝 𝐢𝐧 𝐦𝐞

If you feel lost or want someone to talk to about your problems, the next episode of Edi, D'Talks highlights a key department within the institution that provides students with guidance in tackling their personal and academic endeavors.

Stay tuned for more updates!