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Human-Centric Leadership Practices, AI-Driven Innovation Engagement, Professional Development, and Teacher Performance

Jenifer Cabanducos, Teresita Tumapon
Psychology and Education: A Multidisciplinary Journal · 2026-04-06 · DOI 10.70838/pemj.540508
Token #7526
✓ Verified on SciChain. The record below hashes to exactly the content hash stored on chain for token #7526.
Token#7526 of SciMatic Articles (ERC-1155), fixed supply 1,000,000
Contract0x0E596B4bb924937aA35D9b342A90347ed6F05eF8
Mint transaction0x5cd7d6336a621475e0886d969b558be8d9eec81bc2461d3a698362148d5942e9 · block 12110317
Minted2026-10-02 11:55 UTC
Hash on chain0x8cfdb854f46ee9da3ffc4884ba8b3f7273a2ef1450f78168494c7914b1dc34d1
SHA-256 of the record0x8cfdb854f46ee9da3ffc4884ba8b3f7273a2ef1450f78168494c7914b1dc34d1
Article today Matches the anchored record.

Allocation at minting

HolderWalletMintedHolds now
SciMatic (10%) 0xc35709AB…277732 100,000 100,000
Jenifer Cabanducos 0xaAF8258b…1C48D5 450,000 450,000
Teresita Tumapon 0x7AbcAD39…F8Da99 450,000 450,000

The anchored record

Canonical JSON (keys sorted, no spaces, UTF-8). Its SHA-256 is the hash on chain.

{"abstract":"This study examined the influence of Human-Centric Leadership Practices, AI-Driven Innovation Engagement, and Professional Development on Teacher Performance among public elementary school teachers in selected districts of Cagayan de Oro City, Philippines, during the School Year 2025-2026. Anchored on Human-Centric Leadership Theory, the Technology Acceptance Model, Experiential Learning Theory, and Social Cognitive Theory, the research addressed existing gaps by integrating leadership, technology engagement, and professional learning as interconnected determinants of teacher performance in a digitally evolving educational context. Using a quantitative, predictive-correlational research design, data were gathered through researcher-developed survey questionnaires aligned with the Philippine Professional Standards for Teachers (PPST). Teacher performance was assessed in terms of content knowledge and pedagogy, learning environment and diversity of learners, curriculum and planning, and assessment and reporting. Findings revealed that teachers generally perceived a high level of human-centric leadership, particularly in empathy, emotional intelligence, employee well-being, and inclusive and ethical leadership. Teachers also demonstrated moderate to high engagement with AI-driven innovations, including AI tools, educational technologies, and adaptive learning platforms, as well as active participation in professional development activities such as workshops, mentoring and coaching, and pedagogical innovation. Results further indicated significant relationships among human-centric leadership, AI innovation engagement, professional development, and teacher performance. Regression and structural modeling analyses showed that human-centric leadership directly and indirectly predicted teacher performance, with AI-driven innovation engagement and professional development acting as significant mediating variables. The study concludes that leadership grounded in empathy, inclusivity, and ethical practice fosters a supportive environment that enhances teachers’ readiness to adopt AI innovations and engage in continuous professional learning, ultimately improving performance. The findings provide valuable implications for educational leaders and policymakers in strengthening leadership development, professional learning systems, and responsible AI integration to build resilient and future-ready teaching communities.","abstract_source":"original","authors":["Jenifer Cabanducos","Teresita Tumapon"],"date":"2026-04-06","doi":"10.70838/pemj.540508","id":7526,"issue":5,"journal":{"issn":"2822-4353","name":"Psychology and Education: A Multidisciplinary Journal"},"pdf_sha256":"ad97b12f3db4283d2bd03714fe36a2b3d255826443671b56941a545ebdb6ba8c","schema":"scimatic-article/1","title":"Human-Centric Leadership Practices, AI-Driven Innovation Engagement, Professional Development, and Teacher Performance","volume":54}

Check it yourself

1. The record, from the token's own metadata:

printf '%s' "$(curl -s https://scimatic.org/nft/articles/7526.json | jq -r .canonical_record)" | sha256sum

2. The hash stored on chain (contentHash(7526)):

curl -s https://rpc.scimatic.net -H 'content-type: application/json' \
  -d '{"jsonrpc":"2.0","id":1,"method":"eth_call","params":[{"to":"0x0E596B4bb924937aA35D9b342A90347ed6F05eF8","data":"0xf3e0c2900000000000000000000000000000000000000000000000000000000000001d66"},"latest"]}'

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