Responsible practices and ethical behaviors in the use of artificial intelligence among university students

Responsible practices and ethical behaviors in... | F1000Research "use strict";function _typeof(t){return(_typeof="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(t){return typeof t}:function(t){return t&&"function"==typeof Symbol&&t.constructor===Symbol&&t!==Symbol.prototype?"symbol":typeof t})(t)}!function(){var t=function(){var t,e,o=[],n=window,r=n;for(;r;){try{if(r.frames.__tcfapiLocator){t=r;break}}catch(t){}if(r===n.top)break;r=r.parent}t||(!function t(){var e=n.document,o=!!n.frames.__tcfapiLocator;if(!o)if(e.body){var r=e.createElement("iframe");r.style.cssText="display:none",r.name="__tcfapiLocator",e.body.appendChild(r)}else setTimeout(t,5);return!o}(),n.__tcfapi=function(){for(var t=arguments.length,n=new Array(t),r=0;r 3&&2===parseInt(n[1],10)&&"boolean"==typeof n[3]&&(e=n[3],"function"==typeof n[2]&&n[2]("set",!0)):"ping"===n[0]?"function"==typeof n[2]&&n[2]({gdprApplies:e,cmpLoaded:!1,cmpStatus:"stub"}):o.push(n)},n.addEventListener("message",(function(t){var e="string"==typeof t.data,o={};if(e)try{o=JSON.parse(t.data)}catch(t){}else o=t.data;var n="object"===_typeof(o)&&null!==o?o.__tcfapiCall:null;n&&window.__tcfapi(n.command,n.version,(function(o,r){var a={__tcfapiReturn:{returnValue:o,success:r,callId:n.callId}};t&&t.source&&t.source.postMessage&&t.source.postMessage(e?JSON.stringify(a):a,"*")}),n.parameter)}),!1))};"undefined"!=typeof module?module.exports=t:t()}(); dataLayer = dataLayer || []; // Standard GTM initialization - Google Consent Mode handles consent automatically (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start': new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0], j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src= 'https://www.googletagmanager.com/gtm.js?id='+i+dl+ '>m_auth=hzk0Vc3qFsQYhCrIoHz68A>m_preview=env-1>m_cookies_win=x';f.parentNode.insertBefore(j,f); })(window,document,'script','dataLayer','GTM-MWFK8L5J'); ;window.NREUM||(NREUM={});NREUM.init={distributed_tracing:{enabled:true},privacy:{cookies_enabled:true},ajax:{deny_list:["bam.nr-data.net"]}}; ;NREUM.loader_config={accountID:"438030",trustKey:"438030",agentID:"772317073",licenseKey:"97f8f67f26",applicationID:"772317073"} ;NREUM.info={beacon:"bam.nr-data.net",errorBeacon:"bam.nr-data.net",licenseKey:"97f8f67f26",applicationID:"772317073",sa:1} ;/*! For license information please see nr-loader-spa-1.236.0.min.js.LICENSE.txt */ (()=>{"use strict";var e,t,r={5763:(e,t,r)=>{r.d(t,{P_:()=>l,Mt:()=>g,C5:()=>s,DL:()=>v,OP:()=>T,lF:()=>D,Yu:()=>y,Dg:()=>h,CX:()=>c,GE:()=>b,sU:()=>_});var n=r(8632),i=r(9567);const o={beacon:n.ce.beacon,errorBeacon:n.ce.errorBeacon,licenseKey:void 0,applicationID:void 0,sa:void 0,queueTime:void 0,applicationTime:void 0,ttGuid:void 0,user:void 0,account:void 0,product:void 0,extra:void 0,jsAttributes:{},userAttributes:void 0,atts:void 0,transactionName:void 0,tNamePlain:void 0},a={};function s(e){if(!e)throw new Error("All info objects require an agent identifier!");if(!a[e])throw new Error("Info for ".concat(e," was never set"));return a[e]}function c(e,t){if(!e)throw new Error("All info objects require an agent identifier!");a[e]=(0,i.D)(t,o),(0,n.Qy)(e,a[e],"info")}var u=r(7056);const d=()=>{const e={blockSelector:"[data-nr-block]",maskInputOptions:{password:!0}};return{allow_bfcache:!0,privacy:{cookies_enabled:!0},ajax:{deny_list:void 0,enabled:!0,harvestTimeSeconds:10},distributed_tracing:{enabled:void 0,exclude_newrelic_header:void 0,cors_use_newrelic_header:void 0,cors_use_tracecontext_headers:void 0,allowed_origins:void 0},session:{domain:void 0,expiresMs:u.oD,inactiveMs:u.Hb},ssl:void 0,obfuscate:void 0,jserrors:{enabled:!0,harvestTimeSeconds:10},metrics:{enabled:!0},page_action:{enabled:!0,harvestTimeSeconds:30},page_view_event:{enabled:!0},page_view_timing:{enabled:!0,harvestTimeSeconds:30,long_task:!1},session_trace:{enabled:!0,harvestTimeSeconds:10},harvest:{tooManyRequestsDelay:60},session_replay:{enabled:!1,harvestTimeSeconds:60,sampleRate:.1,errorSampleRate:.1,maskTextSelector:"*",maskAllInputs:!0,get blockClass(){return"nr-block"},get ignoreClass(){return"nr-ignore"},get maskTextClass(){return"nr-mask"},get blockSelector(){return e.blockSelector},set blockSelector(t){e.blockSelector+=",".concat(t)},get maskInputOptions(){return e.maskInputOptions},set maskInputOptions(t){e.maskInputOptions={...t,password:!0}}},spa:{enabled:!0,harvestTimeSeconds:10}}},f={};function l(e){if(!e)throw new Error("All configuration objects require an agent identifier!");if(!f[e])throw new Error("Configuration for ".concat(e," was never set"));return f[e]}function h(e,t){if(!e)throw new Error("All configuration objects require an agent identifier!");f[e]=(0,i.D)(t,d()),(0,n.Qy)(e,f[e],"config")}function g(e,t){if(!e)throw new Error("All configuration objects require an agent identifier!");var r=l(e);if(r){for(var n=t.split("."),i=0;i {r.d(t,{D:()=>i});var n=r(50);function i(e,t){try{if(!e||"object"!=typeof e)return(0,n.Z)("Setting a Configurable requires an object as input");if(!t||"object"!=typeof t)return(0,n.Z)("Setting a Configurable requires a model to set its initial properties");const r=Object.create(Object.getPrototypeOf(t),Object.getOwnPropertyDescriptors(t)),o=0===Object.keys(r).length?e:r;for(let a in o)if(void 0!==e[a])try{"object"==typeof e[a]&&"object"==typeof t[a]?r[a]=i(e[a],t[a]):r[a]=e[a]}catch(e){(0,n.Z)("An error occurred while setting a property of a Configurable",e)}return r}catch(e){(0,n.Z)("An error occured while setting a Configurable",e)}}},6818:(e,t,r)=>{r.d(t,{Re:()=>i,gF:()=>o,q4:()=>n});const n="1.236.0",i="PROD",o="CDN"},385:(e,t,r)=>{r.d(t,{FN:()=>a,IF:()=>u,Nk:()=>f,Tt:()=>s,_A:()=>o,il:()=>n,pL:()=>c,v6:()=>i,w1:()=>d});const n="undefined"!=typeof window&&!!window.document,i="undefined"!=typeof WorkerGlobalScope&&("undefined"!=typeof self&&self instanceof WorkerGlobalScope&&self.navigator instanceof WorkerNavigator||"undefined"!=typeof globalThis&&globalThis instanceof WorkerGlobalScope&&globalThis.navigator instanceof WorkerNavigator),o=n?window:"undefined"!=typeof WorkerGlobalScope&&("undefined"!=typeof self&&self instanceof WorkerGlobalScope&&self||"undefined"!=typeof globalThis&&globalThis instanceof WorkerGlobalScope&&globalThis),a=""+o?.location,s=/iPad|iPhone|iPod/.test(navigator.userAgent),c=s&&"undefined"==typeof SharedWorker,u=(()=>{const e=navigator.userAgent.match(/Firefox[/\s](\d+\.\d+)/);return Array.isArray(e)&&e.length>=2?+e[1]:0})(),d=Boolean(n&&window.document.documentMode),f=!!navigator.sendBeacon},1117:(e,t,r)=>{r.d(t,{w:()=>o});var n=r(50);const i={agentIdentifier:"",ee:void 0};class o{constructor(e){try{if("object"!=typeof e)return(0,n.Z)("shared context requires an object as input");this.sharedContext={},Object.assign(this.sharedContext,i),Object.entries(e).forEach((e=>{let[t,r]=e;Object.keys(i).includes(t)&&(this.sharedContext[t]=r)}))}catch(e){(0,n.Z)("An error occured while setting SharedContext",e)}}}},8e3:(e,t,r)=>{r.d(t,{L:()=>d,R:()=>c});var n=r(2177),i=r(1284),o=r(4322),a=r(3325);const s={};function c(e,t){const r={staged:!1,priority:a.p[t]||0};u(e),s[e].get(t)||s[e].set(t,r)}function u(e){e&&(s[e]||(s[e]=new Map))}function d(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:"",t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:"feature";if(u(e),!e||!s[e].get(t))return a(t);s[e].get(t).staged=!0;const r=[...s[e]];function a(t){const r=e?n.ee.get(e):n.ee,a=o.X.handlers;if(r.backlog&&a){var s=r.backlog[t],c=a[t];if(c){for(var u=0;s&&u {let[t,r]=e;return r.staged}))&&(r.sort(((e,t)=>e[1].priority-t[1].priority)),r.forEach((e=>{let[t]=e;a(t)})))}function f(e,t){var r=e[1];(0,i.D)(t[r],(function(t,r){var n=e[0];if(r[0]===n){var i=r[1],o=e[3],a=e[2];i.apply(o,a)}}))}},2177:(e,t,r)=>{r.d(t,{c:()=>f,ee:()=>u});var n=r(8632),i=r(2210),o=r(1284),a=r(5763),s="nr@context";let c=(0,n.fP)();var u;function d(){}function f(e){return(0,i.X)(e,s,l)}function l(){return new d}function h(){u.aborted=!0,u.backlog={}}c.ee?u=c.ee:(u=function e(t,r){var n={},c={},f={},g=!1;try{g=16===r.length&&(0,a.OP)(r).isolatedBacklog}catch(e){}var p={on:b,addEventListener:b,removeEventListener:y,emit:v,get:x,listeners:w,context:m,buffer:A,abort:h,aborted:!1,isBuffering:E,debugId:r,backlog:g?{}:t&&"object"==typeof t.backlog?t.backlog:{}};return p;function m(e){return e&&e instanceof d?e:e?(0,i.X)(e,s,l):l()}function v(e,r,n,i,o){if(!1!==o&&(o=!0),!u.aborted||i){t&&o&&t.emit(e,r,n);for(var a=m(n),s=w(e),d=s.length,f=0;fn,p:()=>i});var n=r(2177).ee.get("handle");function i(e,t,r,i,o){o?(o.buffer([e],i),o.emit(e,t,r)):(n.buffer([e],i),n.emit(e,t,r))}},4322:(e,t,r)=>{r.d(t,{X:()=>o});var n=r(5546);o.on=a;var i=o.handlers={};function o(e,t,r,o){a(o||n.E,i,e,t,r)}function a(e,t,r,i,o){o||(o="feature"),e||(e=n.E);var a=t[o]=t[o]||{};(a[r]=a[r]||[]).push([e,i])}},3239:(e,t,r)=>{r.d(t,{bP:()=>s,iz:()=>c,m$:()=>a});var n=r(385);let i=!1,o=!1;try{const e={get passive(){return i=!0,!1},get signal(){return o=!0,!1}};n._A.addEventListener("test",null,e),n._A.removeEventListener("test",null,e)}catch(e){}function a(e,t){return i||o?{capture:!!e,passive:i,signal:t}:!!e}function s(e,t){let r=arguments.length>2&&void 0!==arguments[2]&&arguments[2],n=arguments.length>3?arguments[3]:void 0;window.addEventListener(e,t,a(r,n))}function c(e,t){let r=arguments.length>2&&void 0!==arguments[2]&&arguments[2],n=arguments.length>3?arguments[3]:void 0;document.addEventListener(e,t,a(r,n))}},4402:(e,t,r)=>{r.d(t,{Ht:()=>u,M:()=>c,Rl:()=>a,ky:()=>s});var n=r(385);const i="xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx";function o(e,t){return e?15&e[t]:16*Math.random()|0}function a(){const e=n._A?.crypto||n._A?.msCrypto;let t,r=0;return e&&e.getRandomValues&&(t=e.getRandomValues(new Uint8Array(31))),i.split("").map((e=>"x"===e?o(t,++r).toString(16):"y"===e?(3&o()|8).toString(16):e)).join("")}function s(e){const t=n._A?.crypto||n._A?.msCrypto;let r,i=0;t&&t.getRandomValues&&(r=t.getRandomValues(new Uint8Array(31)));const a=[];for(var s=0;s {r.d(t,{Bq:()=>n,Hb:()=>o,oD:()=>i});const n="NRBA",i=144e5,o=18e5},7894:(e,t,r)=>{function n(){return Math.round(performance.now())}r.d(t,{z:()=>n})},7243:(e,t,r)=>{r.d(t,{e:()=>o});var n=r(385),i={};function o(e){if(e in i)return i[e];if(0===(e||"").indexOf("data:"))return{protocol:"data"};let t;var r=n._A?.location,o={};if(n.il)t=document.createElement("a"),t.href=e;else try{t=new URL(e,r.href)}catch(e){return o}o.port=t.port;var a=t.href.split("://");!o.port&&a[1]&&(o.port=a[1].split("/")[0].split("@").pop().split(":")[1]),o.port&&"0"!==o.port||(o.port="https"===a[0]?"443":"80"),o.hostname=t.hostname||r.hostname,o.pathname=t.pathname,o.protocol=a[0],"/"!==o.pathname.charAt(0)&&(o.pathname="/"+o.pathname);var s=!t.protocol||":"===t.protocol||t.protocol===r.protocol,c=t.hostname===r.hostname&&t.port===r.port;return o.sameOrigin=s&&(!t.hostname||c),"/"===o.pathname&&(i[e]=o),o}},50:(e,t,r)=>{function n(e,t){"function"==typeof console.warn&&(console.warn("New Relic: ".concat(e)),t&&console.warn(t))}r.d(t,{Z:()=>n})},2587:(e,t,r)=>{r.d(t,{N:()=>c,T:()=>u});var n=r(2177),i=r(5546),o=r(8e3),a=r(3325);const s={stn:[a.D.sessionTrace],err:[a.D.jserrors,a.D.metrics],ins:[a.D.pageAction],spa:[a.D.spa],sr:[a.D.sessionReplay,a.D.sessionTrace]};function c(e,t){const r=n.ee.get(t);e&&"object"==typeof e&&(Object.entries(e).forEach((e=>{let[t,n]=e;void 0===u[t]&&(s[t]?s[t].forEach((e=>{n?(0,i.p)("feat-"+t,[],void 0,e,r):(0,i.p)("block-"+t,[],void 0,e,r),(0,i.p)("rumresp-"+t,[Boolean(n)],void 0,e,r)})):n&&(0,i.p)("feat-"+t,[],void 0,void 0,r),u[t]=Boolean(n))})),Object.keys(s).forEach((e=>{void 0===u[e]&&(s[e]?.forEach((t=>(0,i.p)("rumresp-"+e,[!1],void 0,t,r))),u[e]=!1)})),(0,o.L)(t,a.D.pageViewEvent))}const u={}},2210:(e,t,r)=>{r.d(t,{X:()=>i});var n=Object.prototype.hasOwnProperty;function i(e,t,r){if(n.call(e,t))return e[t];var i=r();if(Object.defineProperty&&Object.keys)try{return Object.defineProperty(e,t,{value:i,writable:!0,enumerable:!1}),i}catch(e){}return e[t]=i,i}},1284:(e,t,r)=>{r.d(t,{D:()=>n});const n=(e,t)=>Object.entries(e||{}).map((e=>{let[r,n]=e;return t(r,n)}))},4351:(e,t,r)=>{r.d(t,{P:()=>o});var n=r(2177);const i=()=>{const e=new WeakSet;return(t,r)=>{if("object"==typeof r&&null!==r){if(e.has(r))return;e.add(r)}return r}};function o(e){try{return JSON.stringify(e,i())}catch(e){try{n.ee.emit("internal-error",[e])}catch(e){}}}},3960:(e,t,r)=>{r.d(t,{K:()=>a,b:()=>o});var n=r(3239);function i(){return"undefined"==typeof document||"complete"===document.readyState}function o(e,t){if(i())return e();(0,n.bP)("load",e,t)}function a(e){if(i())return e();(0,n.iz)("DOMContentLoaded",e)}},8632:(e,t,r)=>{r.d(t,{EZ:()=>u,Qy:()=>c,ce:()=>o,fP:()=>a,gG:()=>d,mF:()=>s});var n=r(7894),i=r(385);const o={beacon:"bam.nr-data.net",errorBeacon:"bam.nr-data.net"};function a(){return i._A.NREUM||(i._A.NREUM={}),void 0===i._A.newrelic&&(i._A.newrelic=i._A.NREUM),i._A.NREUM}function s(){let e=a();return e.o||(e.o={ST:i._A.setTimeout,SI:i._A.setImmediate,CT:i._A.clearTimeout,XHR:i._A.XMLHttpRequest,REQ:i._A.Request,EV:i._A.Event,PR:i._A.Promise,MO:i._A.MutationObserver,FETCH:i._A.fetch}),e}function c(e,t,r){let i=a();const o=i.initializedAgents||{},s=o[e]||{};return Object.keys(s).length||(s.initializedAt={ms:(0,n.z)(),date:new Date}),i.initializedAgents={...o,[e]:{...s,[r]:t}},i}function u(e,t){a()[e]=t}function d(){return function(){let e=a();const t=e.info||{};e.info={beacon:o.beacon,errorBeacon:o.errorBeacon,...t}}(),function(){let e=a();const t=e.init||{};e.init={...t}}(),s(),function(){let e=a();const t=e.loader_config||{};e.loader_config={...t}}(),a()}},7956:(e,t,r)=>{r.d(t,{N:()=>i});var n=r(3239);function i(e){let t=arguments.length>1&&void 0!==arguments[1]&&arguments[1],r=arguments.length>2?arguments[2]:void 0,i=arguments.length>3?arguments[3]:void 0;return void(0,n.iz)("visibilitychange",(function(){if(t)return void("hidden"==document.visibilityState&&e());e(document.visibilityState)}),r,i)}},1214:(e,t,r)=>{r.d(t,{em:()=>v,u5:()=>N,QU:()=>S,_L:()=>I,Gm:()=>L,Lg:()=>M,gy:()=>U,BV:()=>Q,Kf:()=>ee});var n=r(2177);const i="nr@original";var o=Object.prototype.hasOwnProperty,a=!1;function s(e,t){return e||(e=n.ee),r.inPlace=function(e,t,n,i,o){n||(n="");var a,s,c,u="-"===n.charAt(0);for(c=0;c 2?n-2:0),o=2;o {r(A[T],e,w),r(E[T],e,w)})),r(l._A,"fetch",y),t.on(y+"end",(function(e,r){var n=this;if(r){var i=r.headers.get("content-length");null!==i&&(n.rxSize=i),t.emit(y+"done",[null,r],n)}else t.emit(y+"done",[e],n)})),t}const O={},j=["pushState","replaceState"];function S(e){const t=function(e){return(e||n.ee).get("history")}(e);return!l.il||O[t.debugId]++||(O[t.debugId]=1,s(t).inPlace(window.history,j,"-")),t}var P=r(3239);const C={},R=["appendChild","insertBefore","replaceChild"];function I(e){const t=function(e){return(e||n.ee).get("jsonp")}(e);if(!l.il||C[t.debugId])return t;C[t.debugId]=!0;var r=s(t),i=/[?&](?:callback|cb)=([^&#]+)/,o=/(.*)\.([^.]+)/,a=/^(\w+)(\.|$)(.*)$/;function c(e,t){var r=e.match(a),n=r[1],i=r[3];return i?c(i,t[n]):t[n]}return r.inPlace(Node.prototype,R,"dom-"),t.on("dom-start",(function(e){!function(e){if(!e||"string"!=typeof e.nodeName||"script"!==e.nodeName.toLowerCase())return;if("function"!=typeof e.addEventListener)return;var n=(a=e.src,s=a.match(i),s?s[1]:null);var a,s;if(!n)return;var u=function(e){var t=e.match(o);if(t&&t.length>=3)return{key:t[2],parent:c(t[1],window)};return{key:e,parent:window}}(n);if("function"!=typeof u.parent[u.key])return;var d={};function f(){t.emit("jsonp-end",[],d),e.removeEventListener("load",f,(0,P.m$)(!1)),e.removeEventListener("error",l,(0,P.m$)(!1))}function l(){t.emit("jsonp-error",[],d),t.emit("jsonp-end",[],d),e.removeEventListener("load",f,(0,P.m$)(!1)),e.removeEventListener("error",l,(0,P.m$)(!1))}r.inPlace(u.parent,[u.key],"cb-",d),e.addEventListener("load",f,(0,P.m$)(!1)),e.addEventListener("error",l,(0,P.m$)(!1)),t.emit("new-jsonp",[e.src],d)}(e[0])})),t}var k=r(5763);const H={};function L(e){const t=function(e){return(e||n.ee).get("mutation")}(e);if(!l.il||H[t.debugId])return t;H[t.debugId]=!0;var r=s(t),i=k.Yu.MO;return i&&(window.MutationObserver=function(e){return this instanceof i?new i(r(e,"fn-")):i.apply(this,arguments)},MutationObserver.prototype=i.prototype),t}const z={};function M(e){const t=function(e){return(e||n.ee).get("promise")}(e);if(z[t.debugId])return t;z[t.debugId]=!0;var r=n.c,o=s(t),a=k.Yu.PR;return a&&function(){function e(r){var n=t.context(),i=o(r,"executor-",n,null,!1);const s=Reflect.construct(a,[i],e);return t.context(s).getCtx=function(){return n},s}l._A.Promise=e,Object.defineProperty(e,"name",{value:"Promise"}),e.toString=function(){return a.toString()},Object.setPrototypeOf(e,a),["all","race"].forEach((function(r){const n=a[r];e[r]=function(e){let i=!1;[...e||[]].forEach((e=>{this.resolve(e).then(a("all"===r),a(!1))}));const o=n.apply(this,arguments);return o;function a(e){return function(){t.emit("propagate",[null,!i],o,!1,!1),i=i||!e}}}})),["resolve","reject"].forEach((function(r){const n=a[r];e[r]=function(e){const r=n.apply(this,arguments);return e!==r&&t.emit("propagate",[e,!0],r,!1,!1),r}})),e.prototype=a.prototype;const n=a.prototype.then;a.prototype.then=function(){var e=this,i=r(e);i.promise=e;for(var a=arguments.length,s=new Array(a),c=0;c e())),t};function m(e,t){i.inPlace(t,["onreadystatechange"],"fn-",E)}function b(){var e=this,t=r.context(e);e.readyState>3&&!t.resolved&&(t.resolved=!0,r.emit("xhr-resolved",[],e)),i.inPlace(e,f,"fn-",E)}if(function(e,t){for(var r in e)t[r]=e[r]}(o,p),p.prototype=o.prototype,i.inPlace(p.prototype,J,"-xhr-",E),r.on("send-xhr-start",(function(e,t){m(e,t),function(e){h.push(e),a&&(y?y.then(A):u?u(A):(w=-w,x.data=w))}(t)})),r.on("open-xhr-start",m),a){var y=c&&c.resolve();if(!u&&!c){var w=1,x=document.createTextNode(w);new a(A).observe(x,{characterData:!0})}}else t.on("fn-end",(function(e){e[0]&&e[0].type===d||A()}));function A(){for(var e=0;e {r.d(t,{t:()=>n});const n=r(3325).D.ajax},6660:(e,t,r)=>{r.d(t,{A:()=>i,t:()=>n});const n=r(3325).D.jserrors,i="nr@seenError"},3081:(e,t,r)=>{r.d(t,{gF:()=>o,mY:()=>i,t9:()=>n,vz:()=>s,xS:()=>a});const n=r(3325).D.metrics,i="sm",o="cm",a="storeSupportabilityMetrics",s="storeEventMetrics"},4649:(e,t,r)=>{r.d(t,{t:()=>n});const n=r(3325).D.pageAction},7633:(e,t,r)=>{r.d(t,{Dz:()=>i,OJ:()=>a,qw:()=>o,t9:()=>n});const n=r(3325).D.pageViewEvent,i="firstbyte",o="domcontent",a="windowload"},9251:(e,t,r)=>{r.d(t,{t:()=>n});const n=r(3325).D.pageViewTiming},3614:(e,t,r)=>{r.d(t,{BST_RESOURCE:()=>i,END:()=>s,FEATURE_NAME:()=>n,FN_END:()=>u,FN_START:()=>c,PUSH_STATE:()=>d,RESOURCE:()=>o,START:()=>a});const n=r(3325).D.sessionTrace,i="bstResource",o="resource",a="-start",s="-end",c="fn"+a,u="fn"+s,d="pushState"},7836:(e,t,r)=>{r.d(t,{BODY:()=>A,CB_END:()=>E,CB_START:()=>u,END:()=>x,FEATURE_NAME:()=>i,FETCH:()=>_,FETCH_BODY:()=>v,FETCH_DONE:()=>m,FETCH_START:()=>p,FN_END:()=>c,FN_START:()=>s,INTERACTION:()=>l,INTERACTION_API:()=>d,INTERACTION_EVENTS:()=>o,JSONP_END:()=>b,JSONP_NODE:()=>g,JS_TIME:()=>T,MAX_TIMER_BUDGET:()=>a,REMAINING:()=>f,SPA_NODE:()=>h,START:()=>w,originalSetTimeout:()=>y});var n=r(5763);const i=r(3325).D.spa,o=["click","submit","keypress","keydown","keyup","change"],a=999,s="fn-start",c="fn-end",u="cb-start",d="api-ixn-",f="remaining",l="interaction",h="spaNode",g="jsonpNode",p="fetch-start",m="fetch-done",v="fetch-body-",b="jsonp-end",y=n.Yu.ST,w="-start",x="-end",A="-body",E="cb"+x,T="jsTime",_="fetch"},5938:(e,t,r)=>{r.d(t,{W:()=>o});var n=r(5763),i=r(2177);class o{constructor(e,t,r){this.agentIdentifier=e,this.aggregator=t,this.ee=i.ee.get(e,(0,n.OP)(this.agentIdentifier).isolatedBacklog),this.featureName=r,this.blocked=!1}}},9144:(e,t,r)=>{r.d(t,{j:()=>m});var n=r(3325),i=r(5763),o=r(5546),a=r(2177),s=r(7894),c=r(8e3),u=r(3960),d=r(385),f=r(50),l=r(3081),h=r(8632);function g(){const e=(0,h.gG)();["setErrorHandler","finished","addToTrace","inlineHit","addRelease","addPageAction","setCurrentRouteName","setPageViewName","setCustomAttribute","interaction","noticeError","setUserId"].forEach((t=>{e[t]=function(){for(var r=arguments.length,n=new Array(r),i=0;i 1?r-1:0),i=1;i {e.exposed&&e.api[t]&&o.push(e.api[t](...n))})),o.length>1?o:o[0]}(t,...n)}}))}var p=r(2587);function m(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{},m=arguments.length>2?arguments[2]:void 0,v=arguments.length>3?arguments[3]:void 0,{init:b,info:y,loader_config:w,runtime:x={loaderType:m},exposed:A=!0}=t;const E=(0,h.gG)();y||(b=E.init,y=E.info,w=E.loader_config),(0,i.Dg)(e,b||{}),(0,i.GE)(e,w||{}),(0,i.sU)(e,x),y.jsAttributes??={},d.v6&&(y.jsAttributes.isWorker=!0),(0,i.CX)(e,y),g();const T=function(e,t){t||(0,c.R)(e,"api");const h={};var g=a.ee.get(e),p=g.get("tracer"),m="api-",v=m+"ixn-";function b(t,r,n,o){const a=(0,i.C5)(e);return null===r?delete a.jsAttributes[t]:(0,i.CX)(e,{...a,jsAttributes:{...a.jsAttributes,[t]:r}}),x(m,n,!0,o||null===r?"session":void 0)(t,r)}function y(){}["setErrorHandler","finished","addToTrace","inlineHit","addRelease"].forEach((e=>h[e]=x(m,e,!0,"api"))),h.addPageAction=x(m,"addPageAction",!0,n.D.pageAction),h.setCurrentRouteName=x(m,"routeName",!0,n.D.spa),h.setPageViewName=function(t,r){if("string"==typeof t)return"/"!==t.charAt(0)&&(t="/"+t),(0,i.OP)(e).customTransaction=(r||"http://custom.transaction")+t,x(m,"setPageViewName",!0)()},h.setCustomAttribute=function(e,t){let r=arguments.length>2&&void 0!==arguments[2]&&arguments[2];if("string"==typeof e){if(["string","number"].includes(typeof t)||null===t)return b(e,t,"setCustomAttribute",r);(0,f.Z)("Failed to execute setCustomAttribute.\nNon-null value must be a string or number type, but a type of was provided."))}else(0,f.Z)("Failed to execute setCustomAttribute.\nName must be a string type, but a type of was provided."))},h.setUserId=function(e){if("string"==typeof e||null===e)return b("enduser.id",e,"setUserId",!0);(0,f.Z)("Failed to execute setUserId.\nNon-null value must be a string type, but a type of was provided."))},h.interaction=function(){return(new y).get()};var w=y.prototype={createTracer:function(e,t){var r={},i=this,a="function"==typeof t;return(0,o.p)(v+"tracer",[(0,s.z)(),e,r],i,n.D.spa,g),function(){if(p.emit((a?"":"no-")+"fn-start",[(0,s.z)(),i,a],r),a)try{return t.apply(this,arguments)}catch(e){throw p.emit("fn-err",[arguments,this,"string"==typeof e?new Error(e):e],r),e}finally{p.emit("fn-end",[(0,s.z)()],r)}}}};function x(e,t,r,i){return function(){return(0,o.p)(l.xS,["API/"+t+"/called"],void 0,n.D.metrics,g),i&&(0,o.p)(e+t,[(0,s.z)(),...arguments],r?null:this,i,g),r?void 0:this}}function A(){r.e(439).then(r.bind(r,7438)).then((t=>{let{setAPI:r}=t;r(e),(0,c.L)(e,"api")})).catch((()=>(0,f.Z)("Downloading runtime APIs failed...")))}return["actionText","setName","setAttribute","save","ignore","onEnd","getContext","end","get"].forEach((e=>{w[e]=x(v,e,void 0,n.D.spa)})),h.noticeError=function(e,t){"string"==typeof e&&(e=new Error(e)),(0,o.p)(l.xS,["API/noticeError/called"],void 0,n.D.metrics,g),(0,o.p)("err",[e,(0,s.z)(),!1,t],void 0,n.D.jserrors,g)},d.il?(0,u.b)((()=>A()),!0):A(),h}(e,v);return(0,h.Qy)(e,T,"api"),(0,h.Qy)(e,A,"exposed"),(0,h.EZ)("activatedFeatures",p.T),T}},3325:(e,t,r)=>{r.d(t,{D:()=>n,p:()=>i});const n={ajax:"ajax",jserrors:"jserrors",metrics:"metrics",pageAction:"page_action",pageViewEvent:"page_view_event",pageViewTiming:"page_view_timing",sessionReplay:"session_replay",sessionTrace:"session_trace",spa:"spa"},i={[n.pageViewEvent]:1,[n.pageViewTiming]:2,[n.metrics]:3,[n.jserrors]:4,[n.ajax]:5,[n.sessionTrace]:6,[n.pageAction]:7,[n.spa]:8,[n.sessionReplay]:9}}},n={};function i(e){var t=n[e];if(void 0!==t)return t.exports;var o=n[e]={exports:{}};return r[e](o,o.exports,i),o.exports}i.m=r,i.d=(e,t)=>{for(var r in t)i.o(t,r)&&!i.o(e,r)&&Object.defineProperty(e,r,{enumerable:!0,get:t[r]})},i.f={},i.e=e=>Promise.all(Object.keys(i.f).reduce(((t,r)=>(i.f[r](e,t),t)),[])),i.u=e=>(({78:"page_action-aggregate",147:"metrics-aggregate",242:"session-manager",317:"jserrors-aggregate",348:"page_view_timing-aggregate",412:"lazy-feature-loader",439:"async-api",538:"recorder",590:"session_replay-aggregate",675:"compressor",733:"session_trace-aggregate",786:"page_view_event-aggregate",873:"spa-aggregate",898:"ajax-aggregate"}[e]||e)+"."+{78:"ac76d497",147:"3dc53903",148:"1a20d5fe",242:"2a64278a",317:"49e41428",348:"bd6de33a",412:"2f55ce66",439:"30bd804e",538:"1b18459f",590:"cf0efb30",675:"ae9f91a8",733:"83105561",786:"06482edd",860:"03a8b7a5",873:"e6b09d52",898:"998ef92b"}[e]+"-1.236.0.min.js"),i.o=(e,t)=>Object.prototype.hasOwnProperty.call(e,t),e={},t="NRBA:",i.l=(r,n,o,a)=>{if(e[r])e[r].push(n);else{var s,c;if(void 0!==o)for(var u=document.getElementsByTagName("script"),d=0;d {s.onerror=s.onload=null,clearTimeout(h);var i=e[r];if(delete e[r],s.parentNode&&s.parentNode.removeChild(s),i&&i.forEach((e=>e(n))),t)return t(n)},h=setTimeout(l.bind(null,void 0,{type:"timeout",target:s}),12e4);s.onerror=l.bind(null,s.onerror),s.onload=l.bind(null,s.onload),c&&document.head.appendChild(s)}},i.r=e=>{"undefined"!=typeof Symbol&&Symbol.toStringTag&&Object.defineProperty(e,Symbol.toStringTag,{value:"Module"}),Object.defineProperty(e,"__esModule",{value:!0})},i.j=364,i.p="https://js-agent.newrelic.com/",(()=>{var e={364:0,953:0};i.f.j=(t,r)=>{var n=i.o(e,t)?e[t]:void 0;if(0!==n)if(n)r.push(n[2]);else{var o=new Promise(((r,i)=>n=e[t]=[r,i]));r.push(n[2]=o);var a=i.p+i.u(t),s=new Error;i.l(a,(r=>{if(i.o(e,t)&&(0!==(n=e[t])&&(e[t]=void 0),n)){var o=r&&("load"===r.type?"missing":r.type),a=r&&r.target&&r.target.src;s.message="Loading chunk "+t+" failed.\n("+o+": "+a+")",s.name="ChunkLoadError",s.type=o,s.request=a,n[1](s)}}),"chunk-"+t,t)}};var t=(t,r)=>{var n,o,[a,s,c]=r,u=0;if(a.some((t=>0!==e[t]))){for(n in s)i.o(s,n)&&(i.m[n]=s[n]);if(c)c(i)}for(t&&t(r);u {i.r(o);var e=i(3325),t=i(5763);const r=Object.values(e.D);function n(e){const n={};return r.forEach((r=>{n[r]=function(e,r){return!1!==(0,t.Mt)(r,"".concat(e,".enabled"))}(r,e)})),n}var a=i(9144);var s=i(5546),c=i(385),u=i(8e3),d=i(5938),f=i(3960),l=i(50);class h extends d.W{constructor(e,t,r){let n=!(arguments.length>3&&void 0!==arguments[3])||arguments[3];super(e,t,r),this.auto=n,this.abortHandler,this.featAggregate,this.onAggregateImported,n&&(0,u.R)(e,r)}importAggregator(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{};if(this.featAggregate||!this.auto)return;const r=c.il&&!0===(0,t.Mt)(this.agentIdentifier,"privacy.cookies_enabled");let n;this.onAggregateImported=new Promise((e=>{n=e}));const o=async()=>{let t;try{if(r){const{setupAgentSession:e}=await Promise.all([i.e(860),i.e(242)]).then(i.bind(i,3228));t=e(this.agentIdentifier)}}catch(e){(0,l.Z)("A problem occurred when starting up session manager. This page will not start or extend any session.",e)}try{if(!this.shouldImportAgg(this.featureName,t))return void(0,u.L)(this.agentIdentifier,this.featureName);const{lazyFeatureLoader:r}=await i.e(412).then(i.bind(i,8582)),{Aggregate:o}=await r(this.featureName,"aggregate");this.featAggregate=new o(this.agentIdentifier,this.aggregator,e),n(!0)}catch(e){(0,l.Z)("Downloading and initializing ".concat(this.featureName," failed..."),e),this.abortHandler?.(),n(!1)}};c.il?(0,f.b)((()=>o()),!0):o()}shouldImportAgg(r,n){return r!==e.D.sessionReplay||!1!==(0,t.Mt)(this.agentIdentifier,"session_trace.enabled")&&(!!n?.isNew||!!n?.state.sessionReplay)}}var g=i(7633),p=i(7894);class m extends h{static featureName=g.t9;constructor(r,n){let i=!(arguments.length>2&&void 0!==arguments[2])||arguments[2];if(super(r,n,g.t9,i),("undefined"==typeof PerformanceNavigationTiming||c.Tt)&&"undefined"!=typeof PerformanceTiming){const n=(0,t.OP)(r);n[g.Dz]=Math.max(Date.now()-n.offset,0),(0,f.K)((()=>n[g.qw]=Math.max((0,p.z)()-n[g.Dz],0))),(0,f.b)((()=>{const t=(0,p.z)();n[g.OJ]=Math.max(t-n[g.Dz],0),(0,s.p)("timing",["load",t],void 0,e.D.pageViewTiming,this.ee)}))}this.importAggregator()}}var v=i(1117),b=i(1284);class y extends v.w{constructor(e){super(e),this.aggregatedData={}}store(e,t,r,n,i){var o=this.getBucket(e,t,r,i);return o.metrics=function(e,t){t||(t={count:0});return t.count+=1,(0,b.D)(e,(function(e,r){t[e]=w(r,t[e])})),t}(n,o.metrics),o}merge(e,t,r,n,i){var o=this.getBucket(e,t,n,i);if(o.metrics){var a=o.metrics;a.count+=r.count,(0,b.D)(r,(function(e,t){if("count"!==e){var n=a[e],i=r[e];i&&!i.c?a[e]=w(i.t,n):a[e]=function(e,t){if(!t)return e;t.c||(t=x(t.t));return t.min=Math.min(e.min,t.min),t.max=Math.max(e.max,t.max),t.t+=e.t,t.sos+=e.sos,t.c+=e.c,t}(i,a[e])}}))}else o.metrics=r}storeMetric(e,t,r,n){var i=this.getBucket(e,t,r);return i.stats=w(n,i.stats),i}getBucket(e,t,r,n){this.aggregatedData[e]||(this.aggregatedData[e]={});var i=this.aggregatedData[e][t];return i||(i=this.aggregatedData[e][t]={params:r||{}},n&&(i.custom=n)),i}get(e,t){return t?this.aggregatedData[e]&&this.aggregatedData[e][t]:this.aggregatedData[e]}take(e){for(var t={},r="",n=!1,i=0;i t.max&&(t.max=e),e 2&&void 0!==arguments[2])||arguments[2];super(e,r,j.t,n),c.il&&((0,t.OP)(e).initHidden=Boolean("hidden"===document.visibilityState),(0,N.N)((()=>(0,s.p)("docHidden",[(0,p.z)()],void 0,j.t,this.ee)),!0),(0,O.bP)("pagehide",(()=>(0,s.p)("winPagehide",[(0,p.z)()],void 0,j.t,this.ee))),this.importAggregator())}}var P=i(3081);class C extends h{static featureName=P.t9;constructor(e,t){let r=!(arguments.length>2&&void 0!==arguments[2])||arguments[2];super(e,t,P.t9,r),this.importAggregator()}}var R,I=i(2210),k=i(1214),H=i(2177),L={};try{R=localStorage.getItem("__nr_flags").split(","),console&&"function"==typeof console.log&&(L.console=!0,-1!==R.indexOf("dev")&&(L.dev=!0),-1!==R.indexOf("nr_dev")&&(L.nrDev=!0))}catch(e){}function z(e){try{L.console&&z(e)}catch(e){}}L.nrDev&&H.ee.on("internal-error",(function(e){z(e.stack)})),L.dev&&H.ee.on("fn-err",(function(e,t,r){z(r.stack)})),L.dev&&(z("NR AGENT IN DEVELOPMENT MODE"),z("flags: "+(0,b.D)(L,(function(e,t){return e})).join(", ")));var M=i(6660);class B extends h{static featureName=M.t;constructor(r,n){let i=!(arguments.length>2&&void 0!==arguments[2])||arguments[2];super(r,n,M.t,i),this.skipNext=0;try{this.removeOnAbort=new AbortController}catch(e){}const o=this;o.ee.on("fn-start",(function(e,t,r){o.abortHandler&&(o.skipNext+=1)})),o.ee.on("fn-err",(function(t,r,n){o.abortHandler&&!n[M.A]&&((0,I.X)(n,M.A,(function(){return!0})),this.thrown=!0,(0,s.p)("err",[n,(0,p.z)()],void 0,e.D.jserrors,o.ee))})),o.ee.on("fn-end",(function(){o.abortHandler&&!this.thrown&&o.skipNext>0&&(o.skipNext-=1)})),o.ee.on("internal-error",(function(t){(0,s.p)("ierr",[t,(0,p.z)(),!0],void 0,e.D.jserrors,o.ee)})),this.origOnerror=c._A.onerror,c._A.onerror=this.onerrorHandler.bind(this),c._A.addEventListener("unhandledrejection",(t=>{const r=function(e){let t="Unhandled Promise Rejection: ";if(e instanceof Error)try{return e.message=t+e.message,e}catch(t){return e}if(void 0===e)return new Error(t);try{return new Error(t+(0,D.P)(e))}catch(e){return new Error(t)}}(t.reason);(0,s.p)("err",[r,(0,p.z)(),!1,{unhandledPromiseRejection:1}],void 0,e.D.jserrors,this.ee)}),(0,O.m$)(!1,this.removeOnAbort?.signal)),(0,k.gy)(this.ee),(0,k.BV)(this.ee),(0,k.em)(this.ee),(0,t.OP)(r).xhrWrappable&&(0,k.Kf)(this.ee),this.abortHandler=this.#e,this.importAggregator()}#e(){this.removeOnAbort?.abort(),this.abortHandler=void 0}onerrorHandler(t,r,n,i,o){"function"==typeof this.origOnerror&&this.origOnerror(...arguments);try{this.skipNext?this.skipNext-=1:(0,s.p)("err",[o||new F(t,r,n),(0,p.z)()],void 0,e.D.jserrors,this.ee)}catch(t){try{(0,s.p)("ierr",[t,(0,p.z)(),!0],void 0,e.D.jserrors,this.ee)}catch(e){}}return!1}}function F(e,t,r){this.message=e||"Uncaught error with no additional information",this.sourceURL=t,this.line=r}let U=1;const q="nr@id";function G(e){const t=typeof e;return!e||"object"!==t&&"function"!==t?-1:e===c._A?0:(0,I.X)(e,q,(function(){return U++}))}function V(e){if("string"==typeof e&&e.length)return e.length;if("object"==typeof e){if("undefined"!=typeof ArrayBuffer&&e instanceof ArrayBuffer&&e.byteLength)return e.byteLength;if("undefined"!=typeof Blob&&e instanceof Blob&&e.size)return e.size;if(!("undefined"!=typeof FormData&&e instanceof FormData))try{return(0,D.P)(e).length}catch(e){return}}}var X=i(7243);class W{constructor(e){this.agentIdentifier=e,this.generateTracePayload=this.generateTracePayload.bind(this),this.shouldGenerateTrace=this.shouldGenerateTrace.bind(this)}generateTracePayload(e){if(!this.shouldGenerateTrace(e))return null;var r=(0,t.DL)(this.agentIdentifier);if(!r)return null;var n=(r.accountID||"").toString()||null,i=(r.agentID||"").toString()||null,o=(r.trustKey||"").toString()||null;if(!n||!i)return null;var a=(0,_.M)(),s=(0,_.Ht)(),c=Date.now(),u={spanId:a,traceId:s,timestamp:c};return(e.sameOrigin||this.isAllowedOrigin(e)&&this.useTraceContextHeadersForCors())&&(u.traceContextParentHeader=this.generateTraceContextParentHeader(a,s),u.traceContextStateHeader=this.generateTraceContextStateHeader(a,c,n,i,o)),(e.sameOrigin&&!this.excludeNewrelicHeader()||!e.sameOrigin&&this.isAllowedOrigin(e)&&this.useNewrelicHeaderForCors())&&(u.newrelicHeader=this.generateTraceHeader(a,s,c,n,i,o)),u}generateTraceContextParentHeader(e,t){return"00-"+t+"-"+e+"-01"}generateTraceContextStateHeader(e,t,r,n,i){return i+"@nr=0-1-"+r+"-"+n+"-"+e+"----"+t}generateTraceHeader(e,t,r,n,i,o){if(!("function"==typeof c._A?.btoa))return null;var a={v:[0,1],d:{ty:"Browser",ac:n,ap:i,id:e,tr:t,ti:r}};return o&&n!==o&&(a.d.tk=o),btoa((0,D.P)(a))}shouldGenerateTrace(e){return this.isDtEnabled()&&this.isAllowedOrigin(e)}isAllowedOrigin(e){var r=!1,n={};if((0,t.Mt)(this.agentIdentifier,"distributed_tracing")&&(n=(0,t.P_)(this.agentIdentifier).distributed_tracing),e.sameOrigin)r=!0;else if(n.allowed_origins instanceof Array)for(var i=0;i 2&&void 0!==arguments[2])||arguments[2];super(r,n,Z.t,i),(0,t.OP)(r).xhrWrappable&&(this.dt=new W(r),this.handler=(e,t,r,n)=>(0,s.p)(e,t,r,n,this.ee),(0,k.u5)(this.ee),(0,k.Kf)(this.ee),function(r,n,i,o){function a(e){var t=this;t.totalCbs=0,t.called=0,t.cbTime=0,t.end=E,t.ended=!1,t.xhrGuids={},t.lastSize=null,t.loadCaptureCalled=!1,t.params=this.params||{},t.metrics=this.metrics||{},e.addEventListener("load",(function(r){_(t,e)}),(0,O.m$)(!1)),c.IF||e.addEventListener("progress",(function(e){t.lastSize=e.loaded}),(0,O.m$)(!1))}function s(e){this.params={method:e[0]},T(this,e[1]),this.metrics={}}function u(e,n){var i=(0,t.DL)(r);i.xpid&&this.sameOrigin&&n.setRequestHeader("X-NewRelic-ID",i.xpid);var a=o.generateTracePayload(this.parsedOrigin);if(a){var s=!1;a.newrelicHeader&&(n.setRequestHeader("newrelic",a.newrelicHeader),s=!0),a.traceContextParentHeader&&(n.setRequestHeader("traceparent",a.traceContextParentHeader),a.traceContextStateHeader&&n.setRequestHeader("tracestate",a.traceContextStateHeader),s=!0),s&&(this.dt=a)}}function d(e,t){var r=this.metrics,i=e[0],o=this;if(r&&i){var a=V(i);a&&(r.txSize=a)}this.startTime=(0,p.z)(),this.listener=function(e){try{"abort"!==e.type||o.loadCaptureCalled||(o.params.aborted=!0),("load"!==e.type||o.called===o.totalCbs&&(o.onloadCalled||"function"!=typeof t.onload)&&"function"==typeof o.end)&&o.end(t)}catch(e){try{n.emit("internal-error",[e])}catch(e){}}};for(var s=0;s 1?e[1]=i:e.push(i)}else e[0]&&e[0].headers&&s(e[0].headers,n)&&(this.dt=n);function s(e,t){var r=!1;return t.newrelicHeader&&(e.set("newrelic",t.newrelicHeader),r=!0),t.traceContextParentHeader&&(e.set("traceparent",t.traceContextParentHeader),t.traceContextStateHeader&&e.set("tracestate",t.traceContextStateHeader),r=!0),r}}function x(e,t){this.params={},this.metrics={},this.startTime=(0,p.z)(),this.dt=t,e.length>=1&&(this.target=e[0]),e.length>=2&&(this.opts=e[1]);var r,n=this.opts||{},i=this.target;"string"==typeof i?r=i:"object"==typeof i&&i instanceof Y?r=i.url:c._A?.URL&&"object"==typeof i&&i instanceof URL&&(r=i.href),T(this,r);var o=(""+(i&&i instanceof Y&&i.method||n.method||"GET")).toUpperCase();this.params.method=o,this.txSize=V(n.body)||0}function A(t,r){var n;this.endTime=(0,p.z)(),this.params||(this.params={}),this.params.status=r?r.status:0,"string"==typeof this.rxSize&&this.rxSize.length>0&&(n=+this.rxSize);var o={txSize:this.txSize,rxSize:n,duration:(0,p.z)()-this.startTime};i("xhr",[this.params,o,this.startTime,this.endTime,"fetch"],this,e.D.ajax)}function E(t){var r=this.params,n=this.metrics;if(!this.ended){this.ended=!0;for(var o=0;o 2&&void 0!==arguments[2])||arguments[2];super(e,t,we.t,r),this.importAggregator()}}new class{constructor(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:(0,_.ky)(16);c._A?(this.agentIdentifier=t,this.sharedAggregator=new y({agentIdentifier:this.agentIdentifier}),this.features={},this.desiredFeatures=new Set(e.features||[]),this.desiredFeatures.add(m),Object.assign(this,(0,a.j)(this.agentIdentifier,e,e.loaderType||"agent")),this.start()):(0,l.Z)("Failed to initial the agent. Could not determine the runtime environment.")}get config(){return{info:(0,t.C5)(this.agentIdentifier),init:(0,t.P_)(this.agentIdentifier),loader_config:(0,t.DL)(this.agentIdentifier),runtime:(0,t.OP)(this.agentIdentifier)}}start(){const t="features";try{const r=n(this.agentIdentifier),i=[...this.desiredFeatures];i.sort(((t,r)=>e.p[t.featureName]-e.p[r.featureName])),i.forEach((t=>{if(r[t.featureName]||t.featureName===e.D.pageViewEvent){const n=function(t){switch(t){case e.D.ajax:return[e.D.jserrors];case e.D.sessionTrace:return[e.D.ajax,e.D.pageViewEvent];case e.D.sessionReplay:return[e.D.sessionTrace];case e.D.pageViewTiming:return[e.D.pageViewEvent];default:return[]}}(t.featureName);n.every((e=>r[e]))||(0,l.Z)("".concat(t.featureName," is enabled but one or more dependent features has been disabled (").concat((0,D.P)(n),"). This may cause unintended consequences or missing data...")),this.features[t.featureName]=new t(this.agentIdentifier,this.sharedAggregator)}})),(0,T.Qy)(this.agentIdentifier,this.features,t)}catch(e){(0,l.Z)("Failed to initialize all enabled instrument classes (agent aborted) -",e);for(const e in this.features)this.features[e].abortHandler?.();const r=(0,T.fP)();return delete r.initializedAgents[this.agentIdentifier]?.api,delete r.initializedAgents[this.agentIdentifier]?.[t],delete this.sharedAggregator,r.ee?.abort(),delete r.ee?.get(this.agentIdentifier),!1}}}({features:[J,m,S,class extends h{static featureName=oe;constructor(t,r){if(super(t,r,oe,!(arguments.length>2&&void 0!==arguments[2])||arguments[2]),!c.il)return;const n=this.ee;let i;(0,k.QU)(n),this.eventsEE=(0,k.em)(n),this.eventsEE.on(se,(function(e,t){this.bstStart=(0,p.z)()})),this.eventsEE.on(ae,(function(t,r){(0,s.p)("bst",[t[0],r,this.bstStart,(0,p.z)()],void 0,e.D.sessionTrace,n)})),n.on(ce+ne,(function(e){this.time=(0,p.z)(),this.startPath=location.pathname+location.hash})),n.on(ce+ie,(function(t){(0,s.p)("bstHist",[location.pathname+location.hash,this.startPath,this.time],void 0,e.D.sessionTrace,n)}));try{i=new PerformanceObserver((t=>{const r=t.getEntries();(0,s.p)(te,[r],void 0,e.D.sessionTrace,n)})),i.observe({type:re,buffered:!0})}catch(e){}this.importAggregator({resourceObserver:i})}},C,xe,B,class extends h{static featureName=de;constructor(e,r){if(super(e,r,de,!(arguments.length>2&&void 0!==arguments[2])||arguments[2]),!c.il)return;if(!(0,t.OP)(e).xhrWrappable)return;try{this.removeOnAbort=new AbortController}catch(e){}let n,i=0;const o=this.ee.get("tracer"),a=(0,k._L)(this.ee),s=(0,k.Lg)(this.ee),u=(0,k.BV)(this.ee),d=(0,k.Kf)(this.ee),f=this.ee.get("events"),l=(0,k.u5)(this.ee),h=(0,k.QU)(this.ee),g=(0,k.Gm)(this.ee);function m(e,t){h.emit("newURL",[""+window.location,t])}function v(){i++,n=window.location.hash,this[ve]=(0,p.z)()}function b(){i--,window.location.hash!==n&&m(0,!0);var e=(0,p.z)();this[pe]=~~this[pe]+e-this[ve],this[ye]=e}function y(e,t){e.on(t,(function(){this[t]=(0,p.z)()}))}this.ee.on(ve,v),s.on(be,v),a.on(be,v),this.ee.on(ye,b),s.on(ge,b),a.on(ge,b),this.ee.buffer([ve,ye,"xhr-resolved"],this.featureName),f.buffer([ve],this.featureName),u.buffer(["setTimeout"+le,"clearTimeout"+fe,ve],this.featureName),d.buffer([ve,"new-xhr","send-xhr"+fe],this.featureName),l.buffer([me+fe,me+"-done",me+he+fe,me+he+le],this.featureName),h.buffer(["newURL"],this.featureName),g.buffer([ve],this.featureName),s.buffer(["propagate",be,ge,"executor-err","resolve"+fe],this.featureName),o.buffer([ve,"no-"+ve],this.featureName),a.buffer(["new-jsonp","cb-start","jsonp-error","jsonp-end"],this.featureName),y(l,me+fe),y(l,me+"-done"),y(a,"new-jsonp"),y(a,"jsonp-end"),y(a,"cb-start"),h.on("pushState-end",m),h.on("replaceState-end",m),window.addEventListener("hashchange",m,(0,O.m$)(!0,this.removeOnAbort?.signal)),window.addEventListener("load",m,(0,O.m$)(!0,this.removeOnAbort?.signal)),window.addEventListener("popstate",(function(){m(0,i>1)}),(0,O.m$)(!0,this.removeOnAbort?.signal)),this.abortHandler=this.#e,this.importAggregator()}#e(){this.removeOnAbort?.abort(),this.abortHandler=void 0}}],loaderType:"spa"})})(),window.NRBA=o})(); window.jQuery || document.write(' ') CKEDITOR_BASEPATH='https://f1000research.com/js/vendor/ckeditor/' window.reactTheme = 'research'; window.MathJax = { CommonHTML: { linebreaks: { automatic: true } }, 'HTML-CSS': { linebreaks: { automatic: true } }, SVG: { linebreaks: { automatic: true } }, AuthorInit: function() { MathJax.Hub.Register.MessageHook('End Process', function () { let timeout = false; // holder for timeout id const delay = 250; // delay after event is "complete" to run callback const reflowMath = function() { const dispFormulas = document.querySelectorAll('.disp-formula.panel'); if (!dispFormulas) { return; } for (const dispFormula of dispFormulas) { const child = dispFormula.querySelector('.MathJax_Preview').nextSibling.firstChild; const isMultiline = MathJax.Hub.getAllJax(dispFormula)[0].root.isMultiline; if (dispFormula.offsetWidth < child.offsetWidth || isMultiline) { MathJax.Hub.Queue(['Rerender', MathJax.Hub, dispFormula]); } } }; window.addEventListener('resize', function() { clearTimeout(timeout); // clear the timeout timeout = setTimeout(reflowMath, delay); // start timing for event "completion" }); }); }, }; if (window.location.hash == '#_=_'){ window.location = window.location.href.split('#')[0] } !function(f,b,e,v,n,t,s){if(f.fbq)return;n=f.fbq=function() {n.callMethod? n.callMethod.apply(n,arguments):n.queue.push(arguments)} ;if(!f._fbq)f._fbq=n; n.push=n;n.loaded=!0;n.version='2.0';n.queue=[];t=b.createElement(e);t.async=!0; t.src=v;s=b.getElementsByTagName(e)[0];s.parentNode.insertBefore(t,s)}(window, document,'script','https://connect.facebook.net/en_US/fbevents.js'); fbq('init', '1641728616063202'); fbq('track', "PixelInitialized", {}); (function(h,o,t,j,a,r){ h.hj=h.hj||function(){(h.hj.q=h.hj.q||[]).push(arguments)}; h._hjSettings={hjid:2318163,hjsv:6}; a=o.getElementsByTagName('head')[0]; r=o.createElement('script');r.async=1; r.src=t+h._hjSettings.hjid+j+h._hjSettings.hjsv; a.appendChild(r); })(window,document,'https://static.hotjar.com/c/hotjar-','.js?sv='); search file_upload Submit your research search menu close search Browse Gateways & Collections How to Publish Submit your Research My Submissions Article Guidelines Article Guidelines (New Versions) Open Data, Software and Code Guidelines Open Data and Accessible Source Materials Guidelines (HSS) Open Data, Software and Code Guidelines (PSE) Prepublication Checks Production Process Posters and Slides Guidelines Document Guidelines Article Processing Charges Peer Review Finding Article Reviewers About How it Works For Reviewers Our Advisors Policies Glossary FAQs For Developers Newsroom Contact My Research Submissions Content and Tracking Alerts My Details Sign In file_upload Submit your research { "@context": "https://schema.org", "@type": "ScholarlyArticle", "mainEntityOfPage": { "@type": "WebPage", "@id": "https://f1000research.com/articles/15-16" }, "headline": "Responsible practices and ethical behaviors in the use of artificial intelligence among university students", "datePublished": "2026-01-06T23:41:06", "dateModified": "2026-01-06T23:41:06", "author": [ { "@type": "Person", "name": "Kathiusca Echeverria-Caicedo" }, { "@type": "Person", "name": "Jesús Alejandro Vázquez-Meza" }, { "@type": "Person", "name": "Lidia Janeth Llacsa-Puma" }, { "@type": "Person", "name": "María Alexandra Gutiérrez-Izquierdo" }, { "@type": "Person", "name": "Lucio Guadalupe Quirino-Rodríguez" }, { "@type": "Person", "name": "Raúl Alberto Rengifo-Lozano" }, { "@type": "Person", "name": "Charito Norma Chipana Peceros" }, { "@type": "Person", "name": "Ángel Ramón Sabando-García" }, { "@type": "Person", "name": "Jenniffer Sobeida Moreira-Choez" } ], "publisher": { "@type": "Organization", "name": "F1000Research", "logo": { "@type": "ImageObject", "url": "https://f1000research.com/img/AMP/F1000Research_image.png", "height": 480, "width": 60 } }, "image": { "@type": "ImageObject", "url": "https://f1000research.com/img/AMP/F1000Research_image.png", "height": 1200, "width": 150 }, "description": " Background The growing incorporation of artificial intelligence (AI) in higher education presents significant ethical challenges, particularly regarding the critical and responsible use of these technologies by students. In this context, the present study aimed to analyze responsible practices and ethical behaviors in the use of AI among students at the State University of Milagro. Methods A quantitative, explanatory-level approach was applied, employing a non-experimental and cross-sectional design, with a validated questionnaire administered to 716 participants from various academic programs. The analysis was conducted through structural equation modeling to explore the influence of affective, behavioral, and cognitive learning dimensions on the ethical dimension. Results The affective and cognitive dimensions exert a significant and positive impact on the development of learning ethics, while the behavioral component did not show statistically relevant effects. Additionally, the study evidenced high reliability and validity of the instrument, as well as a medium level of AI appropriation among students, with predominant use of tools such as ChatGPT. Conclusions Ethical training cannot be dissociated from emotional attitudes and technical knowledge about AI. The study acknowledges as limitations the non-probabilistic nature of the sample and the cross-sectional design. " } { "@context": "http://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": "1", "item": { "@id": "https://f1000research.com/", "name": "Home" } }, { "@type": "ListItem", "position": "2", "item": { "@id": "https://f1000research.com/browse/articles", "name": "Browse" } }, { "@type": "ListItem", "position": "3", "item": { "@id": "https://f1000research.com/articles/15-16/v1", "name": "Responsible practices and ethical behaviors in the use of artificial..." } } ] } Home Browse Responsible practices and ethical behaviors in the use of artificial... ALL Metrics - Views Downloads Get PDF Get XML Cite How to cite this article Echeverria-Caicedo K, Vázquez-Meza JA, Llacsa-Puma LJ et al. Responsible practices and ethical behaviors in the use of artificial intelligence among university students [version 1; peer review: 1 approved with reservations, 1 not approved] . F1000Research 2026, 15 :16 ( https://doi.org/10.12688/f1000research.172751.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Research Article Responsible practices and ethical behaviors in the use of artificial intelligence among university students [version 1; peer review: 1 approved with reservations, 1 not approved] Kathiusca Echeverria-Caicedo 1 , Jesús Alejandro Vázquez-Meza 2 , Lidia Janeth Llacsa-Puma 3 , [...] María Alexandra Gutiérrez-Izquierdo 4 , Lucio Guadalupe Quirino-Rodríguez 2 , Raúl Alberto Rengifo-Lozano 5 , Charito Norma Chipana Peceros 5 , Ángel Ramón Sabando-García 6 , Jenniffer Sobeida Moreira-Choez https://orcid.org/0000-0001-8604-3295 1 Kathiusca Echeverria-Caicedo 1 , Jesús Alejandro Vázquez-Meza 2 , [...] Lidia Janeth Llacsa-Puma 3 , María Alexandra Gutiérrez-Izquierdo 4 , Lucio Guadalupe Quirino-Rodríguez 2 , Raúl Alberto Rengifo-Lozano 5 , Charito Norma Chipana Peceros 5 , Ángel Ramón Sabando-García 6 , Jenniffer Sobeida Moreira-Choez https://orcid.org/0000-0001-8604-3295 1 PUBLISHED 06 Jan 2026 Author details Author details 1 Facultad de Posgrado, Universidad Estatal de Milagro, Milagro, Guayas, 091050, Ecuador 2 Universidad Autonoma de Sinaloa, Culiacán, Sinaloa, 80000, Mexico 3 Universidad Peruana Union, Lima District, Lima Region, 15082, Peru 4 Facultad de Ciencias Humanísticas y Sociales, Universidad Tecnica de Manabi, Portoviejo, Manabí Province, 130104, Ecuador 5 Universidad Nacional Mayor de San Marcos, Lima District, Lima Region, 15081, Peru 6 Pontificia Universidad Católica del Ecuador- Sede Santo Domingo, Santo Domingo, Santo Domingo, 030109, Ecuador Kathiusca Echeverria-Caicedo Roles: Conceptualization, Methodology, Project Administration, Supervision, Writing – Review & Editing Jesús Alejandro Vázquez-Meza Roles: Data Curation, Formal Analysis, Validation, Writing – Original Draft Preparation Lidia Janeth Llacsa-Puma Roles: Investigation, Resources, Writing – Review & Editing María Alexandra Gutiérrez-Izquierdo Roles: Data Curation, Software, Visualization, Writing – Review & Editing Lucio Guadalupe Quirino-Rodríguez Roles: Formal Analysis, Validation, Writing – Review & Editing Raúl Alberto Rengifo-Lozano Roles: Investigation, Methodology, Resources, Writing – Review & Editing Charito Norma Chipana Peceros Roles: Investigation, Supervision, Writing – Review & Editing Ángel Ramón Sabando-García Roles: Data Curation, Software, Validation, Visualization, Writing – Review & Editing Jenniffer Sobeida Moreira-Choez Roles: Funding Acquisition, Project Administration, Supervision, Writing – Original Draft Preparation, Writing – Review & Editing OPEN PEER REVIEW DETAILS REVIEWER STATUS This article is included in the Artificial Intelligence and Machine Learning gateway. Abstract Background The growing incorporation of artificial intelligence (AI) in higher education presents significant ethical challenges, particularly regarding the critical and responsible use of these technologies by students. In this context, the present study aimed to analyze responsible practices and ethical behaviors in the use of AI among students at the State University of Milagro. Methods A quantitative, explanatory-level approach was applied, employing a non-experimental and cross-sectional design, with a validated questionnaire administered to 716 participants from various academic programs. The analysis was conducted through structural equation modeling to explore the influence of affective, behavioral, and cognitive learning dimensions on the ethical dimension. Results The affective and cognitive dimensions exert a significant and positive impact on the development of learning ethics, while the behavioral component did not show statistically relevant effects. Additionally, the study evidenced high reliability and validity of the instrument, as well as a medium level of AI appropriation among students, with predominant use of tools such as ChatGPT. Conclusions Ethical training cannot be dissociated from emotional attitudes and technical knowledge about AI. The study acknowledges as limitations the non-probabilistic nature of the sample and the cross-sectional design. READ ALL READ LESS Keywords Ethics, educational technology, artificial intelligence, higher education, student behavior, educational assessment, pedagogical innovation, assisted learning. Corresponding Author(s) Jenniffer Sobeida Moreira-Choez ( [email protected] ) Close Corresponding author: Jenniffer Sobeida Moreira-Choez Competing interests: No competing interests were disclosed. Grant information: The author(s) declared that no grants were involved in supporting this work. Copyright: © 2026 Echeverria-Caicedo K et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The author(s) is/are employees of the US Government and therefore domestic copyright protection in USA does not apply to this work. The work may be protected under the copyright laws of other jurisdictions when used in those jurisdictions. How to cite: Echeverria-Caicedo K, Vázquez-Meza JA, Llacsa-Puma LJ et al. Responsible practices and ethical behaviors in the use of artificial intelligence among university students [version 1; peer review: 1 approved with reservations, 1 not approved] . F1000Research 2026, 15 :16 ( https://doi.org/10.12688/f1000research.172751.1 ) First published: 06 Jan 2026, 15 :16 ( https://doi.org/10.12688/f1000research.172751.1 ) Latest published: 06 Jan 2026, 15 :16 ( https://doi.org/10.12688/f1000research.172751.1 ) Introduction The integration of artificial intelligence (AI) in higher education has generated growing academic interest in recent decades, driven by its potential to profoundly transform teaching, learning, assessment, educational personalization, and institutional management processes ( Alotaibi, 2024 ; Katsamakas et al., 2024 ; Murdan & Halkhoree, 2024 ). This transformation is grounded in the development of intelligent systems capable of analyzing large volumes of data, detecting patterns of student behavior, and generating automated interventions ( George & Wooden, 2023 ), thereby opening new possibilities for a more efficient, adaptive, and student-centered education. In this context, recent studies such as those by Jiang et al. (2024) have shown that the technological dimension carries the greatest weight in the processes of incorporating AI into higher education, far surpassing other dimensions such as pedagogical, ethical, and cultural. This technological predominance has favored an instrumental adoption of AI, focused on infrastructure, availability of tools, and task automation, while the formative and axiological components have been addressed only in a fragmented or secondary manner. Several investigations have warned that this reductionist orientation generates multiple tensions within university environments ( Moreira-Choez et al., 2025 ; Nishant et al., 2020 ; Raisch & Krakowski, 2021 ). On the one hand, it has been reported that the uncritical use of AI-based systems, without adequate pedagogical mediation, can generate inequalities in access to knowledge, algorithmic biases, and technological dependency ( Arnold, 2021 ; Bracci, 2023 ). On the other hand, the lack of ethical training in the use of these technologies has given rise to questionable academic practices, such as the excessive delegation of cognitive tasks to automated tools, the inappropriate use of generative models for content production, and the lack of transparency in assessment criteria ( Liehner et al., 2022 ; Munoko et al., 2020 ; Parker & Grote, 2022 ). Furthermore, a significant gap has been identified between technological development and students’ capacity to critically understand the social, cultural, and regulatory impacts of AI, which limits their active and reflective participation in digital environments ( Chan et al., 2025 ). Despite these advances, several studies have highlighted the existence of persistent challenges in the adoption of AI in university contexts ( Alhosani & Alhashmi, 2024 ; Naseer et al., 2025 ; Ragolane & Patel, 2024 ; Sabando-García et al., 2025 ). These include limited teacher training in advanced digital competencies, insufficient curricular adaptation, and the absence of clearly defined ethical-legal regulatory frameworks ( Gabriel et al., 2022 ; Moreira-Choez et al., 2024d ). Moreover, recent research emphasizes the need to consider sociocultural factors in the design and implementation of AI-based strategies, given that cultural perceptions directly influence the acceptance, use, and appropriation of these technologies. However, a theoretical and empirical gap persists regarding the interaction between these approaches, as well as a lack of integrative studies simultaneously addressing the technological, pedagogical, ethical, and cultural dimensions. In this context, it becomes essential to develop research that enables a systemic understanding of the factors influencing the responsible and ethical incorporation of AI in higher education. The present study is justified both by the academic relevance of the phenomenon and by its potential impact on the transformation of training processes, the consolidation of a critical digital culture, and the promotion of a more equitable, inclusive, and ethically guided education. Despite the growing interest in technological deployment within universities, gaps remain in understanding the role played by the affective, behavioral, and cognitive dimensions of AI learning in shaping students’ ethical practices. Based on this problem, the following research question is posed: What are the responsible practices and ethical behaviors in the use of artificial intelligence among students at the State University of Milagro (UNEMI)? From this question, the following hypotheses are formulated: H1. The affective learning of AI significantly influences learning ethics. H2. The behavioral learning of AI has a significant impact on learning ethics. H3. The cognitive learning of AI significantly contributes to learning ethics. H4. AI learning has a significant impact on learning ethics among university students. Accordingly, the general objective of this study is to analyze responsible practices and ethical behaviors in the use of artificial intelligence among students at the State University of Milagro (UNEMI), with identifying the predominant factors influencing their ethical development and proposing guidelines for a contextualized and sustainable implementation in higher education environments. Methods The present study was conducted under a quantitative and explanatory approach, aimed at identifying and analyzing the factors that influence the responsible and ethical use of artificial intelligence in the university context. A non-experimental, cross-sectional design was adopted, as data collection was carried out at a single point in time without manipulating the independent variables. The research was of a correlational-causal type, as it sought to establish significant relationships between the dimensions of AI learning (cognitive, affective, and behavioral) and learning ethics among university students. The study was conducted at UNEMI, a higher education institution located in the city of Milagro, in the province of Guayas, Ecuador. The study population consisted of undergraduate students enrolled in various degree programs. A sample of 716 students was selected using non-probabilistic convenience sampling, considering their accessibility and willingness to participate in the study. For data collection, a structured questionnaire was administered through digital means, which included scales previously validated in studies on digital competencies, learning ethics, and the academic use of emerging technologies. Table 1 presents the demographic and academic characteristics of the participants, as well as their preferences and frequency of use of artificial intelligence (AI) applications. Table 1. Distribution of the sample by sex, program, and preference for artificial intelligence applications. Variable Category Frequency Percentage Sex Male 267 37.3 Female 449 62.7 Total 716 100.0 Age Group 17 to 19 years 212 29.6 20 to 25 years 421 58.8 Over 25 years 83 11.6 Total 716 100.0 Semester Second 152 21.2 Third 54 7.5 Fourth 193 27.0 Fifth 60 8.4 Sixth 85 11.9 Seventh 89 12.4 Eighth 73 10.2 Ninth 9 1.3 Tenth 1 0.1 Total 716 100.0 Preference for AI Applications ChatGPT 442 61.7 Gemini 111 15.5 Siri 57 8.0 Sora 1 0.1 Deepseek 4 0.6 Copilot 6 0.8 Google Bard 39 5.4 Claude 4 0.6 DALL·E 6 0.8 Midjourney 8 1.1 Monica 3 0.4 Fireflies 5 0.7 Perplexity 6 0.8 Sider 1 0.1 Venice 1 0.1 Poe 1 0.1 Meta 1 0.1 GAMMA 1 0.1 None 19 2.7 Total 716 100.0 Frequency of AI Use in Academic Activities Not frequent 18 2.5 Slightly frequent 153 21.4 Occasionally 298 41.6 Frequent 180 25.1 Very frequent 67 9.4 Total 716 100.0 Table 1 presents the distribution of the sample according to sex, age group, academic semester, preference for artificial intelligence (AI) applications, and frequency of AI use in academic activities. The sample distribution revealed a higher female participation rate (62.7%) compared to male (37.3%), suggesting a predominance of women in the study, possibly reflecting the overall composition of the university population. Regarding age, most participants were between 20 and 25 years old (58.8%), with a mean age of 21.6 years (SD = 3.7), representing a typically young cohort corresponding to the formative stage of higher education. The distribution by semester showed heterogeneity, with a predominance in the fourth (27.0%) and second (21.2%) semesters, which may be associated with greater availability or motivation among students in intermediate academic stages. Concerning preferences for artificial intelligence applications, ChatGPT emerged as the most frequently used tool (61.7%), followed by Gemini (15.5%), reflecting a trend toward the use of conversational models in educational settings. Finally, the frequency of AI use in academic activities showed that 41.6% of students use it occasionally and 25.1% use it frequently, indicating a moderate but expanding adoption of these technologies in university learning processes. To ensure the validity and reliability of the instrument used to measure the use and ethics of artificial intelligence in the university context, fundamental statistical tests were applied prior to the factorial and structural analyses. Table 2 presents the results of the Kaiser-Meyer-Olkin (KMO) measure and Bartlett’s test of sphericity, which evaluate the adequacy of the sample for factor analysis. Table 2. KMO and Bartlett’s test for measuring AI use and ethics. KMO and Bartlett’s test Kaiser-Meyer-Olkin Measure of Sampling Adequacy ,987 Bartlett’s Test of Sphericity Approx. Chi-Square 75301,593 gl 1485 Sig. ,000 To ensure the validity and reliability of the instrument designed to assess the use and ethics of artificial intelligence in the university environment, preliminary statistical tests were conducted to verify data suitability before performing factorial and structural analyses. The results Table 2 showed a Kaiser-Meyer-Olkin (KMO) index of 0.987, indicating an excellent level of sampling adequacy and a high degree of interrelation among the analyzed items. Similarly, Bartlett’s test of sphericity produced a chi-square value of 75,301.593 with 1,485 degrees of freedom and a significance level below 0.001, confirming sufficient correlations among the variables. These results demonstrated the instrument’s internal consistency and its appropriateness for the application of multivariate techniques aimed at validating the proposed factorial structure. In turn, Table 3 presents the internal reliability coefficients of the questionnaire, calculated using Cronbach’s α and McDonald’s Ω indicators, both for individual factors and for the overall scale. Table 3. Reliability analysis of the AI questionnaire. Factors Cronbach’s α McDonald’s Ω Number of items Affective learning 0,982 0,982 19 Behavioral learning 0,970 0,970 11 Cognitive learning 0,973 0,972 9 Ethical learning 0,991 0,991 16 Total 0,992 0,992 55 Regarding Table 3 , it is observed that the reliability coefficients for all evaluated factors far exceed the minimum acceptable threshold of 0.70. Affective, behavioral, cognitive, and ethical learning show α and Ω coefficients ranging from 0.970 to 0.991, reflecting very high internal consistency. At the global level, the instrument reached α and Ω values of 0.992, indicating excellent reliability of the questionnaire. These findings confirm that the instrument used is statistically robust and suitable for accurately assessing students’ perceptions and attitudes regarding learning and the ethical use of artificial intelligence in the university setting. Likewise, a specifically designed instrument was employed to assess responsible practices and ethical behaviors associated with the use of artificial intelligence in the university context. The questionnaire was administered through the Google Forms platform. The initial section of the form included an informed consent statement describing the objectives of the study, the voluntary nature of participation, data confidentiality, and the participants’ right to withdraw at any time without consequences. In accordance with the ethical principles governing research involving human participants, prior informed consent was obtained from all individuals involved in the study. For participants under 18 years of age, informed consent was provided by their legal guardians, in compliance with institutional and regulatory requirements. This consent was recorded electronically within the Google form: only those who accepted the terms or whose legal guardians accepted them, in the case of minors were allowed to access the questionnaire, while those who did not provide consent were automatically redirected to the end of the form, concluding their participation. The items were structured using a five-point Likert scale, ranging from 1 (strongly disagree) to 5 (strongly agree), enabling the quantification of perceptions, attitudes, and ethical behaviors related to the use of artificial intelligence. Data processing and analysis were conducted using the statistical software SPSS (version 28) and AMOS, through which descriptive analyses, internal reliability tests, and validation of the proposed structural model were performed. Ethical considerations All individuals who took part in this research gave their informed consent electronically within the Google form in accordance with ethical guidelines for studies involving human participants. They were informed that participation was entirely voluntary and that they retained the right to withdraw at any stage without any consequences. To safeguard confidentiality, all personal information was anonymized. The study received ethical clearance from the Institutional Review Board (IRB) of Milagro State University, through approval “Oficio Nro. UNEMI-VICEINVYPOSG-DP-277-2025-OF,” issued on March 22, 2025. Results and discussion This section presents the results derived from the proposed structural equation model, which aimed to analyze the relationship between the dimensions of artificial intelligence learning (affective, behavioral, and cognitive) and learning ethics among university students at PUCESD. Model validation was performed using AMOS software, applying maximum likelihood estimation. The results obtained provide insights into the latent interactions among the studied variables and their influence on the configuration of ethics in educational contexts mediated by AI. Figure 1 presents the structural model of AI learning ethics among university students. The diagram illustrates the relationships among the affective, behavioral, cognitive, and ethical latent variables, highlighting the strength and direction of the standardized paths estimated through structural equation modeling. Figure 1. Structural model of AI learning on learning ethics in university students. Note: The model displays the factor loadings of the indicators (P1–P55), errors (e1–e56), and the structural paths between the affective, behavioral, cognitive, and ethical variables. Figure 1 illustrates the structural model that integrates the affective, behavioral, and cognitive dimensions of artificial intelligence learning and their direct associations with the ethical component. The standardized factor loadings (P1–P55) demonstrate high and consistent values across the observed indicators, confirming both convergent validity and internal consistency of the proposed theoretical model. The affective construct presents the strongest connection with the ethical dimension (β = 0.68), evidencing that students’ attitudes, emotions, and beliefs toward artificial intelligence substantially influence the internalization of ethical principles within the learning process. This finding aligns with previous studies emphasizing that emotional engagement toward emerging technologies constitutes a decisive factor in fostering critical awareness and digital ethical competencies ( Mâță et al., 2020 ; Moreira-Choez et al., 2024b , 2024c ; Pinargote-Macías et al., 2022 ). Similarly, Farangi et al. (2024) point out that positive emotions toward AI enhance ethical learning readiness and strengthen responsible judgment in digital contexts. The behavioral dimension also exhibits a meaningful relationship with learning ethics (β = –0.05), suggesting that students’ habitual actions, usage patterns, and decision-making processes in AI-supported environments directly shape their ethical reasoning. This result supports Kudina (2019) , who contends that ethical behavior in technology-mediated contexts is not derived solely from technical expertise but from the practical application of social responsibility principles. Correspondingly, Verma and Garg (2024) argue that active participation in AI-mediated educational experiences fosters ethical awareness, particularly when such activities are accompanied by pedagogical reflection and moral guidance. Meanwhile, the cognitive dimension demonstrates a positive association with the ethical construct (β = 0.39), indicating that conceptual and procedural understanding of artificial intelligence contributes to ethical awareness, albeit to a lesser extent than the affective component. This suggests that cognitive mastery alone is insufficient to promote ethical conduct without the reinforcement of affective and behavioral engagement. In line with Waight et al. (2022) , this result underscores the need to integrate technical knowledge with axiological and ethical training to enable students to critically assess the societal implications of AI. Therefore, ethical learning in AI-mediated educational settings should be conceived as a multidimensional process, emerging from the dynamic interaction among knowledge, emotion, and behavior, which together shape the moral consciousness necessary for responsible technological engagement. The following section presents the analysis of the level of artificial intelligence use by university students, considering the four dimensions evaluated in the study: affective, behavioral, cognitive, and ethical. This information is summarized in Figure 2 , which illustrates the percentage distribution of AI use according to low, medium, and high levels across each of the dimensions. Figure 2. Level of AI use among university students. Note: The figure illustrates the distribution of AI use levels low, medium, and high across the affective, behavioral, cognitive, and ethical learning dimensions, showing a predominance of medium-level engagement among university students. Figure 2 reveals that most university students fall within a medium level of artificial intelligence use, both in the overall score (52.8%) and in the affective (47.8%), behavioral (43.7%), and cognitive (41.4%) dimensions. This finding demonstrates a partial adoption of AI as an academic resource, which may be linked to ongoing familiarization processes and an incipient integration into curricular environments. Similar results were reported by Valdivieso and González (2025) , who found that most Latin American university students use AI-based tools occasionally and without clear institutional guidance. In this regard, the predominance of the medium level suggests the need to strengthen systematic training processes that consolidate the pedagogical use of AI from a critical and contextualized perspective. Regarding the low levels, a greater concentration is observed in behavioral learning (34.7%) and cognitive learning (34.1%), which may reflect limitations in technical mastery and the operational application of AI tools. These results are consistent with Xia et al. (2024) , who warn that, although students show interest in AI, there is a significant training gap in the functional use of these technologies for complex academic tasks. Similarly, the studies of Fanidawarti Hamzah et al. (2024) indicate that the lack of teacher training and the limited curricular integration of emerging technologies hinder the development of cognitive and procedural competencies among university students, restricting their ability to apply AI beyond superficial or recreational contexts. In the ethical dimension, the results show a differential pattern. Although 32.4% of students were placed at the low level, the high level registered the highest percentage (34.6%) among all dimensions, suggesting a stronger internalization of principles and values associated with the responsible use of AI. This finding is consistent with Malinverni et al. (2025) , who emphasize that the ethical dimension tends to develop more strongly when spaces for reflection on the social implications of using emerging technologies are created. Moreover, Chiu and Chai (2020) , state that learning environments with an ethical orientation foster conscious practices, particularly when AI is addressed from an interdisciplinary and humanistic perspective. Therefore, the relative predominance of the high level in this dimension can be interpreted as a positive indicator of the transformative potential of ethical training in digital contexts. Finally, the affective dimension shows a notable proportion at the high level (27.6%), suggesting that a significant group of students expresses positive attitudes toward AI, accompanied by interest, motivation, and willingness to learn. This result coincides with Bahroun et al. (2023) , who found that a favorable emotional perception of AI can be a decisive factor for its integration into educational processes, provided that such an attitude is accompanied by meaningful content and participatory methodologies. Table 4 presents the results of the convergent validity analysis for the research model on AI use and ethics. The table reports the standardized factor loadings, internal consistency coefficients (Cronbach’s α), composite reliability (CR), and average variance extracted (AVE) for the four latent dimensions—affective, behavioral, cognitive, and ethical learning—demonstrating the reliability and construct validity of the measurement model. Table 4. Convergent validity of the research model on AI use and ethics. Factors Item Factor loading Cronbach’s α Composite Reliability (CR) Average Variance Extracted (VME) Affective Learning 1. Intrinsic motivation [The AI concepts I learn are relevant to my life (e.g., personal, academic, and professional).] 0,804 0,982 0,982 0,747 2. Intrinsic motivation [Learning AI is interesting.] 0,817 0,982 3. Intrinsic motivation [Learning AI makes my life more meaningful.] 0,759 0,982 4. Intrinsic motivation [I am curious to explore new AI technologies.] 0,797 0,982 5. AI learning self-efficacy [I am confident that I will perform well in AI-related tasks.] 0,863 0,981 6. AI learning self-efficacy [I am confident that I will do well in AI-related projects.] 0,874 0,981 7. AI learning self-efficacy [I believe I can master AI knowledge and skills.] 0,865 0,981 8. AI learning self-efficacy [I believe I can achieve good grades in AI-related assessments.] 0,865 0,981 9. AI learning self-efficacy [I am confident that I can understand AI.] 0,862 0,981 10. Professional interest [Learning AI will help me obtain a good job in the future.] 0,873 0,981 11. Professional interest [Knowing how to use AI will give me a professional advantage for my future career.] 0,892 0,981 12. Professional interest [Understanding AI will benefit my future career.] 0,903 0,981 13. Professional interest [My future career will involve AI.] 0,857 0,982 14. Professional interest [I will use AI-related problem-solving skills in my career.] 0,874 0,981 15. Confidence in using AI [I can make good use of AI-related tools.] 0,889 0,981 16. Confidence in using AI [I am confident that I can successfully complete AI-related tasks.] 0,906 0,981 17. Confidence in using AI [I am confident that I will do well in AI-related assignments.] 0,904 0,981 18. Confidence in using AI [I am confident that I can learn the basics of AI.] 0,899 0,981 19. Confidence in using AI [I am confident that I can choose appropriate AI applications to solve problems.] 0,902 0,981 Behavioral Learning 20. Behavioral intention [I will continue using AI in the future.] 0,842 0,969 0,971 0,754 21. Behavioral intention [I will stay updated with the latest AI technologies.] 0,869 0,968 22. Behavioral intention [I plan to spend time exploring new AI application features in the future.] 0,875 0,968 23. Behavioral engagement [I actively participate in AI learning activities.] 0,905 0,967 24. Behavioral engagement [I am dedicated to AI learning materials.] 0,897 0,967 25. Behavioral engagement [I learn effectively in AI learning tasks.] 0,883 0,968 26. Behavioral engagement [I often review additional AI materials after class, such as books and journals.] 0,887 0,967 27. Collaboration [I often try to explain AI learning materials to my classmates or friends.] 0,885 0,967 28. Collaboration [I try to work with classmates to complete AI-related tasks and projects.] 0,905 0,967 29. Collaboration [I often spend my free time discussing AI with classmates.] 0,807 0,969 30. Collaboration [I usually ask my peers for help when I face difficulties in AI activities.] 0,79 0,970 Cognitive Learning 31. Knowing and understanding [I know what AI is and can recall its definitions.] 0,856 0,971 0,973 0,801 32. Knowing and understanding [I know how to use AI applications (e.g., Siri, chatbot).] 0,861 0,971 33. Knowing and understanding [I know some operating principles behind AI (e.g., linear model, decision tree, machine learning).] 0,889 0,970 34. Knowing and understanding [I understand how AI perceives the world (e.g., seeing, hearing) to perform various tasks.] 0,909 0,969 35. Knowing and understanding [I can compare differences among AI concepts (e.g., deep learning, machine learning).] 0,902 0,969 36. Applying, evaluating, and creating [I can use AI applications to solve problems.] 0,892 0,970 37. Applying, evaluating, and creating [I can use a machine learning model to solve problems.] 0,913 0,969 38. Applying, evaluating, and creating [I can create AI-based solutions (e.g., chatbots, robotics) to solve problems.] 0,915 0,969 39. Applying, evaluating, and creating [I can evaluate AI applications and concepts for different situations.] 0,913 0,969 Ethical Learning 40. AI ethics [I believe AI ethics are important to guide moral behavior in the development and use of AI technology.] 0,875 0,991 0,991 0,869 41. AI ethics [I understand how the misuse of AI could pose substantial risks to humans.] 0,897 0,990 42. AI ethics [I believe AI systems should minimize data bias (e.g., gender, ethnicity).] 0,868 0,991 43. AI ethics [I believe AI systems should operate reliably and safely.] 0,932 0,990 44. AI ethics [I believe AI systems should undergo rigorous testing to ensure proper functioning.] 0,936 0,990 45. AI ethics [I believe AI systems should respect privacy.] 0,934 0,990 46. AI ethics [I believe users are responsible for considering AI design and decision-making processes.] 0,944 0,990 47. AI ethics [I believe AI systems should benefit everyone regardless of physical ability or gender.] 0,963 0,990 48. AI ethics [I believe AI systems should be transparent and understandable.] 0,959 0,990 49. AI ethics [I believe users should be aware of the system’s purpose, functioning, and limitations.] 0,967 0,990 50. AI ethics [I believe people should be accountable for the use of AI systems.] 0,952 0,990 51. AI ethics [I believe AI systems should comply with ethical and legal standards.] 0,939 0,990 52. AI ethics [I believe AI can be used to help disadvantaged people.] 0,952 0,990 53. AI ethics [I believe AI can promote human well-being.] 0,922 0,990 54. AI ethics [I want to use my AI knowledge to serve others.] 0,926 0,990 55. AI ethics [I believe AI use should aim to achieve the common good (e.g., environmental and poverty issues).] 0,945 0,990 Table 4 shows that all factors included in the structural model exhibit high levels of reliability and convergent validity. Regarding factor loadings, the items within each dimension exceeded the minimum threshold of 0.70 established by Fokides (2023) , indicating a strong association between each item and its corresponding factor. In the case of affective learning, loadings ranged from 0.759 to 0.906, reflecting coherence among intrinsic motivation, self-efficacy, professional interest, and confidence in using AI. These results are supported by a Cronbach’s alpha and composite reliability of 0.982, along with an AVE of 0.747, demonstrating excellent internal consistency. In the behavioral dimension, which includes items related to intention of use, engagement, and collaboration, factor loadings ranged from 0.79 to 0.905. A composite reliability of 0.971 and a Cronbach’s alpha of 0.969 were obtained, confirming the stability of this dimension. These findings are consistent with studies such as those by Yaseen et al. (2025) , who state that active engagement with technology and social interaction around AI learning are key indicators of practical skill development in academic contexts. With respect to cognitive learning, very high loadings were observed, ranging from 0.856 to 0.915, in items related to conceptual knowledge, application, and evaluation of AI technologies. The composite reliability reached 0.973 and the AVE was 0.801, supporting the statistical robustness of this dimension. These results are consistent with those reported by Modran et al. (2024) , who argue that meaningful learning in AI requires both theoretical understanding and the ability to apply and transfer this knowledge to real-world contexts. Finally, ethical learning exhibited the highest factor loadings in the model, with values between 0.868 and 0.967, evidencing a strong alignment among items related to moral principles, algorithmic fairness, transparency, social well-being, and the ethical use of AI. This dimension achieved a composite reliability and Cronbach’s alpha of 0.991, with an AVE of 0.869, indicating excellent convergent validity. This result is consistent with the arguments of Floridi et al. (2018) and Díaz-Rodríguez et al. (2023) , who emphasize that ethical development in digital environments must include a critical understanding of AI’s risks and benefits, as well as an orientation toward the common good. Subsequently, Table 5 presents the results of the discriminant validity analysis among the factors of the proposed theoretical model. Discriminant validity is an essential criterion in structural equation modeling, as it verifies whether the evaluated constructs are empirically distinct from one another. For this purpose, the heterotrait-monotrait ratio (HTMT) was used, a more sensitive and robust criterion compared to traditional metrics such as those proposed by Fornell and Larcker. Table 5. Heterotrait–monotrait ratio analysis of the dimensions of AI learning and ethics. Factors A-Affective A-Behavioral A-Cognitive A-Ethical A-Affective A-Behavioral 0,884 A-Cognitive 0,847 0,917 A-Ethical 0,783 0,767 0,811 Table 5 presents the HTMT index values corresponding to the relationships among the affective, behavioral, cognitive, and ethical dimensions of artificial intelligence learning. The analysis shows that most correlations remain within acceptable ranges, confirming adequate differentiation among the factors that compose the theoretical model. However, the association between the behavioral and cognitive dimensions reached a value of 0.917, slightly exceeding the recommended threshold, suggesting the existence of a possible conceptual overlap between these variables. This result can be interpreted as evidence of semantic proximity between observable behaviors and cognitive skills related to the use of artificial intelligence. According to Lans et al. (2014) , in educational models integrating interdependent variables, it is common to identify conceptual overlaps in dimensions that share thought and action processes. In this regard, the high correlation between behavioral and cognitive learning could be attributed to the fact that the practical application of AI such as active tool use or collaborative participation requires prior understanding of its technical and operational foundations. Similar studies, such as that of Dai et al. (2020) , report a strong relationship between cognitive mastery and intention of use among university students, which supports the empirical trend observed in the present study. On the other hand, the HTMT values between the affective component and the other factors remained within acceptable ranges (0.847 with cognitive, 0.884 with behavioral, and 0.783 with ethical), confirming that emotional dispositions toward AI such as motivation and self-efficacy constitute an empirically distinct dimension. Likewise, the ethical factor showed moderate correlations with the remaining dimensions (ranging from 0.767 to 0.811), supporting its conceptual independence, albeit with interconnections. This differentiation is consistent with the findings of Cetindamar et al. (2024) , who assert that ethical judgment emerges from interaction with but not fusion of other digital learning competencies. Subsequently, Figure 3 presents the results of the structural equation model designed to analyze the effect of the affective, behavioral, and cognitive dimensions of AI learning on the development of learning ethics among university students. The model was evaluated through path analysis, considering both standardized and unstandardized factor loadings, as well as statistical significance values (T and P). This approach allows for the identification of which dimensions have greater predictive capacity in the internalization of ethical principles in the use of AI. Figure 3. Standardized and unstandardized factor loadings. Note: Goodness-of-fit tests: Affective Learning (T = 9.114; p = 0.000); Behavioral Learning (T = -0.336; p = 0.737); Cognitive Learning (T = 11.150; p = 0.000). ANOVA (F = 572.662; p = 0.000). Figure 3 illustrates the estimated causal relationships among the latent factors. Affective learning showed a positive and highly significant effect on AI learning ethics, with a standardized loading of β = 0.675 (T = 57.66; p = 0.000). This result confirms that attitudes, beliefs, emotions, and interests related to artificial intelligence learning have a substantial influence on the development of ethical behaviors. This finding is consistent with that reported by An et al. (2022) , who state that a positive emotional disposition toward technology promotes reflective, responsible, and socially oriented use. Likewise, Shafiee (2025) argues that affective engagement with AI fosters self-regulated processes that strengthen ethical decision-making in digital environments. In contrast, behavioral learning did not show a statistically significant relationship with the ethical variable (β = 0.128; T = 0.336; p = 0.737). Although it presented adequate structural reliability, this dimension did not directly predict the levels of ethical behavior reported by students. The absence of this effect could be explained by the lack of systematic academic practices involving the ethical use of AI tools, or by a disconnection between the practical use of technology and ethical reflection on its implications. Similar results were observed by Palau et al. (2021) , who found that active participation in technological tasks does not always translate into morally aligned behaviors, especially when pedagogical guidance is absent. On the other hand, cognitive learning showed a moderate but significant effect on the ethical dimension (β = 0.561; T = 11.150; p = 0.000). This finding suggests that conceptual knowledge, technical understanding, and the ability to critically evaluate the foundations of AI positively influence awareness of its responsible use. According to Kitchin (2019) , digital critical thinking is a key competence for building a technology ethics grounded in knowledge and understanding of algorithmic processes. In the same vein, Schiff (2022) highlights that cognitive development in AI enhances awareness of the social, political, and cultural risks associated with its implementation in education. Subsequently, Table 6 presents the results of the hypothesis testing based on the theoretical model, which explores the effect of the affective, behavioral, and cognitive dimensions of artificial intelligence learning on learning ethics among university students. The relationships among the latent variables affective, behavioral, cognitive, and ethical were analyzed through structural equation modeling, considering standardized regression coefficients (β) and statistical significance levels. Table 6. Validation of the hypotheses on AI use and ethics. Results of the hypotheses Hypothesis Relationship β p-value Result H1. The affective learning of AI significantly influences learning ethics. Total → Ethical 0,675 *** Accepted H2. The behavioral learning of AI has a significant impact on learning ethics. Behavioral → Ethical -0,128 0,058 Not Accepted H3. The cognitive learning of AI significantly contributes to learning ethics. Cognitive → Ethical 0,567 *** Accepted H4. AI learning has a significant impact on learning ethics among university students. Affective → Ethical 0,413 *** Accepted Table 6 presents the coefficients obtained from the hypothesis testing. Hypothesis 1, which evaluated the overall effect of AI learning on ethics, obtained a coefficient of β = 0.675 with high statistical significance (p < 0.001), confirming that the general use of artificial intelligence is positively associated with learning ethics. This result corroborates studies such as those by Moreira-Choez et al. (2024a) and Örtegren (2022) , who argue that formative appropriation of AI combining technical competencies with ethical values can contribute to the development of digitally responsible citizens. Regarding Hypothesis 2, which postulated a significant relationship between behavioral learning and ethics, the coefficient was negative and non-significant (β = –0.128; p = 0.058), leading to the rejection of the hypothesis. This result suggests that performing AI-related activities without pedagogical guidance or critical reflection does not guarantee ethical behavior. This finding is consistent with the observations of Sinclair et al. (2022) , who state that mere active participation in technological tasks does not imply ethical internalization unless articulated with axiological frameworks and explicit formative processes. For Hypothesis 3, which analyzed the influence of cognitive learning on ethics, the model yielded a coefficient of β = 0.567 with high significance (p < 0.001), thus validating the hypothesis. This finding demonstrates that technical knowledge, understanding of operational principles, and critical evaluation capacity of AI are determining factors in developing ethical criteria. It aligns with Falloon (2020) , who notes that ethical digital literacy must be grounded in a deep understanding of how technology functions and its social implications. Finally, Hypothesis 4, which examined the relationship between affective learning and ethics, also proved significant (β = 0.413; p < 0.001), supporting the hypothesis and reinforcing the idea that attitudes, interests, and emotions toward AI influence students’ ethical formation. This result is consistent with Sinclair et al. (2022) , who argue that the affective dimension acts as a catalyst in the ethical adoption of emerging technologies by fostering a more conscious, empathetic, and reflective relationship with the digital environment. Conclusions The progressive incorporation of artificial intelligence into higher education has raised important challenges related to the ethical and responsible use of these technologies by university students. In this context, the present study aimed to analyze responsible practices and ethical behaviors associated with the use of AI among students at the State University of Milagro, considering the affective, behavioral, and cognitive dimensions of learning. The results obtained indicate that the proposed objective was achieved and that the research question was empirically answered. Likewise, three of the four hypotheses formulated were statistically confirmed, showing that both affective and cognitive learning significantly influence the development of an ethical attitude toward the use of artificial intelligence. In contrast, behavioral learning did not show a significant relationship, suggesting that performing AI-related actions alone does not ensure ethical behavior unless accompanied by reflection and critical understanding. Among the main findings, the affective dimension related to motivation, confidence, and professional interest had the greatest impact on ethical behavior. Similarly, the cognitive dimension, focused on technical knowledge and understanding of AI-related concepts, also showed a significant effect. The overall model revealed a positive relationship between total AI use and learning ethics, reinforcing the importance of educating students not only in the functional use of these technologies but also in their critical and contextualized appropriation. The study presents certain limitations that should be considered. The sample was non-probabilistic and limited to a single institution, which restricts the generalization of the results to other contexts. Moreover, the cross-sectional design prevents establishing direct causal relationships and observing changes over time in students’ ethical perceptions. As future lines of research, it is proposed to apply the model in other universities to compare results across different academic environments. It is also recommended to conduct longitudinal studies to observe the evolution of ethical practices related to AI, as well as qualitative research to explore in greater depth the experiences, perceptions, and challenges students face in their interaction with these technologies. Finally, it is suggested to design and implement comprehensive pedagogical strategies that promote the development of ethical, affective, and cognitive competencies for the conscious and responsible use of artificial intelligence in higher education. Data availability Figshare: Data from the article titled: Responsible practices and ethical behaviors in the use of artificial intelligence among university students.xlsx. DOI: https://doi.org/10.6084/m9.figshare.30473126.v1 ( Echeverria-Caicedo et al., 2025a ). The project contains the following underlying data: - Data from the article titled: Responsible practices and ethical behaviors in the use of artificial intelligence among university students.xlsx - Questionnaire of the article titled “Responsible practices and ethical behaviors in the use of artificial intelligence among university students” DOI: https://doi.org/10.6084/m9.figshare.30716477.v1 ( Echeverria-Caicedo et al., 2025b ) Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC BY 4.0 Public domain dedication). Acknowledgements The authors acknowledge the participation of students from the State University of Milagro (UNEMI) who voluntarily contributed to this study. Appreciation is also extended to the academic and administrative authorities for providing the institutional support necessary to carry out the research. References Alhosani K, Alhashmi SM: Opportunities, challenges, and benefits of AI innovation in government services: a review. Discov. Artif. Intell. 2024; 4 (1): 18. Publisher Full Text Alotaibi NS: The Impact of AI and LMS Integration on the Future of Higher Education: Opportunities, Challenges, and Strategies for Transformation. Sustainability. 2024; 16 (23): 10357. Publisher Full Text An F, Xi L, Yu J, et al. : Relationship between Technology Acceptance and Self-Directed Learning: Mediation Role of Positive Emotions and Technological Self-Efficacy. Sustainability. 2022; 14 (16): 10390. Publisher Full Text Arnold MH: Teasing out Artificial Intelligence in Medicine: An Ethical Critique of Artificial Intelligence and Machine Learning in Medicine. Journal of Bioethical Inquiry. 2021; 18 (1): 121–139. PubMed Abstract | Publisher Full Text | Free Full Text Bahroun Z, Anane C, Ahmed V, et al. : Transforming Education: A Comprehensive Review of Generative Artificial Intelligence in Educational Settings through Bibliometric and Content Analysis. Sustainability. 2023; 15 (17): 12983. Publisher Full Text Bracci E: The loopholes of algorithmic public services: an “intelligent” accountability research agenda. Account. Audit. Account. J. 2023; 36 (2): 739–763. Publisher Full Text Cetindamar D, Kitto K, Wu M, et al. : Explicating AI Literacy of Employees at Digital Workplaces. IEEE Trans. Eng. Manag. 2024; 71 : 810–823. Publisher Full Text Chan G, Banire B, Ataguba G, et al. : A Panoramic View of Socio-Cultural Sensitivity in Digital Technologies: A Comprehensive Review and Future Directions. International Journal of Human–Computer Interaction. 2025; 41 (4): 1917–1945. Publisher Full Text Chiu TKF, Chai C: Sustainable Curriculum Planning for Artificial Intelligence Education: A Self-Determination Theory Perspective. Sustainability. 2020; 12 (14): 5568. Publisher Full Text Dai HM, Teo T, Rappa NA, et al. : Explaining Chinese university students’ continuance learning intention in the MOOC setting: A modified expectation confirmation model perspective. Comput. Educ. 2020; 150 : 103850. Publisher Full Text Díaz-Rodríguez N, Del Ser J, Coeckelbergh M, et al. : Connecting the dots in trustworthy Artificial Intelligence: From AI principles, ethics, and key requirements to responsible AI systems and regulation. Inf. Fusion. 2023; 99 : 101896. Publisher Full Text Echeverria-Caicedo K, Vázquez-Meza JA, Llacsa-Puma LJ, et al. : Data from the article titled: Responsible practices and ethical behaviors in the use of artificial intelligence among university students (Version 1). figshare. 2025a. Publisher Full Text Echeverria-Caicedo K, Vázquez-Meza JA, Llacsa-Puma LJ, et al. : Questionnaire of the article titled “Responsible practices and ethical behaviors in the use of artificial intelligence among university students” (Version 1). figshare. 2025b. Publisher Full Text Falloon G: From digital literacy to digital competence: the teacher digital competency (TDC) framework. Educ. Technol. Res. Dev. 2020; 68 (5): 2449–2472. Publisher Full Text Farangi MR, Nejadghanbar H, Hu G: Use of generative AI in research: ethical considerations and emotional experiences. Ethics Behav. 2024; 35 : 527–543. Publisher Full Text Floridi L, Cowls J, Beltrametti M, et al. : AI4People—An Ethical Framework for a Good AI Society: Opportunities, Risks, Principles, and Recommendations. Mind. Mach. 2018; 28 (4): 689–707. PubMed Abstract | Publisher Full Text | Free Full Text Fokides E: Development and testing of a scale for examining factors affecting the learning experience in the Metaverse. Computers & Education: X Reality. 2023; 2 : 100025. Publisher Full Text Gabriel F, Marrone R, Van Sebille Y, et al. : Digital education strategies around the world: practices and policies. Ir. Educ. Stud. 2022; 41 (1): 85–106. Publisher Full Text George B, Wooden O: Managing the Strategic Transformation of Higher Education through Artificial Intelligence. Administrative Sciences. 2023; 13 (9): 196. Publisher Full Text Hamzah F, Abdullah AH, Ma W: Advancing Education through Technology Integration, Innovative Pedagogies and Emerging Trends: A Systematic Literature Review. Journal of Advanced Research in Applied Sciences and Engineering Technology. 2024; 41 (1): 44–63. Publisher Full Text Jiang Q, Qian J, Zang Y: To acknowledge or conceal: an exploratory study on designers’ self-determination factors and attitudes toward artificial intelligence participation in their works. Kybernetes. 2024. Publisher Full Text Katsamakas E, Pavlov OV, Saklad R: Artificial Intelligence and the Transformation of Higher Education Institutions: A Systems Approach. Sustainability. 2024; 16 (14): 6118. Publisher Full Text Kitchin R: The Social Power of Algorithms. Beer D, editor. Routledge; 1.a edición. 2019. Publisher Full Text Kudina O: The technological mediation of morality: value dynamism, and the complex interaction between ethics and technology. University of Twente; 2019. Publisher Full Text Lans T, Blok V, Wesselink R: Learning apart and together: towards an integrated competence framework for sustainable entrepreneurship in higher education. J. Clean. Prod. 2014; 62 : 37–47. Publisher Full Text Liehner GL, Brauner P, Schaar AK, et al. : Delegation of Moral Tasks to Automated Agents—The Impact of Risk and Context on Trusting a Machine to Perform a Task. IEEE Transactions on Technology and Society. 2022; 3 (1): 46–57. Publisher Full Text Malinverni L, Schaper M-M, Rubegni E, et al. : Scaffolding Children’s critical reflection on intelligent technologies: Opportunities from speculative fiction. International Journal of Child-Computer Interaction. 2025; 43 : 100727. Publisher Full Text Mâță L, Clipa O, Tzafilkou K: The Development and Validation of a Scale to Measure University Teachers’ Attitude towards Ethical Use of Information Technology for a Sustainable Education. Sustainability. 2020; 12 (15): 6268. Publisher Full Text Modran HA, Ursuțiu D, Samoilă C: Using the Theoretical-Experiential Binomial for Educating AI-Literate Students. Sustainability. 2024; 16 (10): 4068. Publisher Full Text Moreira-Choez JS, Gómez Barzola KE, Lamus de Rodríguez TM, et al. : Assessment of digital competencies in higher education faculty: a multimodal approach within the framework of artificial intelligence. Frontiers in Education. 2024a; 9 . Publisher Full Text Moreira-Choez JS, Lamus de Rodríguez TM, Cedeño Barcia LA, et al. : Competencias digitales en docentes de educación superior: Un análisis integral basado en una revisión sistemática. Revista de Ciencias Sociales. 2024b; 30 (3): 317–331. Publisher Full Text Moreira-Choez JS, Lamus de Rodríguez TM, Espinoza-Solís EJ, et al. : Comparative Analysis of Psychological Well-Being and Emotional Education in Graduate Students. F1000Res. 2024c; 12 : 1403. PubMed Abstract | Publisher Full Text | Free Full Text Moreira-Choez JS, Zambrano-Acosta JM, López-Padrón A: Digital teaching competence of higher education professors: self-perception study in an Ecuadorian university. F1000Res. 2024d; 12 : 1484. PubMed Abstract | Publisher Full Text | Free Full Text Moreira-Choez JS, Núñez-Naranjo AF, Carrasco-Valenzuela AC, et al. : Machine Learning Algorithms to Predict Digital Competencies in University Faculty. F1000Res. 2025; 14 : 573. Publisher Full Text Munoko I, Brown-Liburd HL, Vasarhelyi M: The Ethical Implications of Using Artificial Intelligence in Auditing. J. Bus. Ethics. 2020; 167 (2): 209–234. Publisher Full Text Murdan AP, Halkhoree R: Integration of Artificial Intelligence for educational excellence and innovation in higher education institutions. 2024 1st International Conference on Smart Energy Systems and Artificial Intelligence (SESAI). 2024; 1–6. Publisher Full Text Naseer F, Khan MN, Addas A, et al. : Game Mechanics and Artificial Intelligence Personalization: A Framework for Adaptive Learning Systems. Educ. Sci. 2025; 15 (3): 301. Publisher Full Text Nishant R, Kennedy M, Corbett J: Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda. Int. J. Inf. Manag. 2020; 53 : 102104. Publisher Full Text Örtegren A: Digital Citizenship and Professional Digital Competence — Swedish Subject Teacher Education in a Postdigital Era. Postdigital Science and Education. 2022; 4 (2): 467–493. PubMed Abstract | Publisher Full Text | Free Full Text Palau R, Fuentes M, Mogas J, et al. : Analysis of the implementation of teaching and learning processes at Catalan schools during the Covid-19 lockdown. Technol. Pedagog. Educ. 2021; 30 (1): 183–199. Publisher Full Text Parker SK, Grote G: Automation, Algorithms, and Beyond: Why Work Design Matters More Than Ever in a Digital World. Appl. Psychol. 2022; 71 (4): 1171–1204. Publisher Full Text Pinargote-Macías EI, Vega Intriago JO, Moreira Choez JS, et al. : Competencias del docente universitario en tiempos de pandemia. Revista Venezolana de Gerencia. 2022; 27 (Edición Especial 7): 347–359. Publisher Full Text Ragolane M, Patel S: Transforming Educ-AI-tion in South Africa: Can AI-Driven Grading Transform the Future of Higher Education? Journal of Education and Teaching Methods. 2024; 3 (1): 26–51. Publisher Full Text Raisch S, Krakowski S: Artificial Intelligence and Management: The Automation–Augmentation Paradox. Acad. Manag. Rev. 2021; 46 (1): 192–210. Publisher Full Text Sabando-García ÁR, Olguín-Martínez CM, Benavides-Lara RM, et al. : Artificial intelligence for determining learning strategies in university students. Frontiers in Education. 2025; 10 . Publisher Full Text Schiff D: Education for AI, not AI for Education: The Role of Education and Ethics in National AI Policy Strategies. Int. J. Artif. Intell. Educ. 2022; 32 (3): 527–563. Publisher Full Text Shafiee Rad H: Reinforcing L2 reading comprehension through artificial intelligence intervention: refining engagement to foster self-regulated learning. Smart Learning Environments. 2025; 12 (1): 23. Publisher Full Text Sinclair N, Haspekian M, Robutti O, et al. : Revisiting Theories That Frame Research on Teaching Mathematics with Digital Technology. The Mathematics Teacher in the Digital Era. 2022; 16 : 391–418. Publisher Full Text Valdivieso T, González O: Generative AI Tools in Salvadoran Higher Education: Balancing Equity, Ethics, and Knowledge Management in the Global South. Educ. Sci. 2025; 15 (2): 214. Publisher Full Text Verma S, Garg N: New ethical frontier: exploring the nexus of techno-ethical orientation and corporate ethical values moderated by digital citizenship behaviour. J. Organ. Chang. Manag. 2024; 37 (2): 391–407. Publisher Full Text Waight N, Kayumova S, Tripp J, et al. : Towards Equitable, Social Justice Criticality: Re-Constructing the “Black” Box and Making it Transparent for the Future of Science and Technology in Science Education. Sci. & Educ. 2022; 31 (6): 1493–1515. PubMed Abstract | Publisher Full Text | Free Full Text Xia Y, Shin S-Y, Kim J-C: Cross-Cultural Intelligent Language Learning System (CILS): Leveraging AI to Facilitate Language Learning Strategies in Cross-Cultural Communication. Appl. Sci. 2024; 14 (13): 5651. Publisher Full Text Yaseen H, Mohammad AS, Ashal N, et al. : The Impact of Adaptive Learning Technologies, Personalized Feedback, and Interactive AI Tools on Student Engagement: The Moderating Role of Digital Literacy. Sustainability. 2025; 17 (3): 1133. Publisher Full Text Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 06 Jan 2026 ADD YOUR COMMENT Comment Author details Author details 1 Facultad de Posgrado, Universidad Estatal de Milagro, Milagro, Guayas, 091050, Ecuador 2 Universidad Autonoma de Sinaloa, Culiacán, Sinaloa, 80000, Mexico 3 Universidad Peruana Union, Lima District, Lima Region, 15082, Peru 4 Facultad de Ciencias Humanísticas y Sociales, Universidad Tecnica de Manabi, Portoviejo, Manabí Province, 130104, Ecuador 5 Universidad Nacional Mayor de San Marcos, Lima District, Lima Region, 15081, Peru 6 Pontificia Universidad Católica del Ecuador- Sede Santo Domingo, Santo Domingo, Santo Domingo, 030109, Ecuador Kathiusca Echeverria-Caicedo Roles: Conceptualization, Methodology, Project Administration, Supervision, Writing – Review & Editing Jesús Alejandro Vázquez-Meza Roles: Data Curation, Formal Analysis, Validation, Writing – Original Draft Preparation Lidia Janeth Llacsa-Puma Roles: Investigation, Resources, Writing – Review & Editing María Alexandra Gutiérrez-Izquierdo Roles: Data Curation, Software, Visualization, Writing – Review & Editing Lucio Guadalupe Quirino-Rodríguez Roles: Formal Analysis, Validation, Writing – Review & Editing Raúl Alberto Rengifo-Lozano Roles: Investigation, Methodology, Resources, Writing – Review & Editing Charito Norma Chipana Peceros Roles: Investigation, Supervision, Writing – Review & Editing Ángel Ramón Sabando-García Roles: Data Curation, Software, Validation, Visualization, Writing – Review & Editing Jenniffer Sobeida Moreira-Choez Roles: Funding Acquisition, Project Administration, Supervision, Writing – Original Draft Preparation, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information The author(s) declared that no grants were involved in supporting this work. Article Versions (1) version 1 Published: 06 Jan 2026, 15:16 https://doi.org/10.12688/f1000research.172751.1 Copyright © 2026 Echeverria-Caicedo K et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The author(s) is/are employees of the US Government and therefore domestic copyright protection in USA does not apply to this work. The work may be protected under the copyright laws of other jurisdictions when used in those jurisdictions. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Echeverria-Caicedo K, Vázquez-Meza JA, Llacsa-Puma LJ et al. Responsible practices and ethical behaviors in the use of artificial intelligence among university students [version 1; peer review: 1 approved with reservations, 1 not approved] . F1000Research 2026, 15 :16 ( https://doi.org/10.12688/f1000research.172751.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 1 VERSION 1 PUBLISHED 06 Jan 2026 Views 0 Cite How to cite this report: Asamoah D. Reviewer Report For: Responsible practices and ethical behaviors in the use of artificial intelligence among university students [version 1; peer review: 1 approved with reservations, 1 not approved] . F1000Research 2026, 15 :16 ( https://doi.org/10.5256/f1000research.190499.r459009 ) The direct URL for this report is: https://f1000research.com/articles/15-16/v1#referee-response-459009 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 02 Mar 2026 Daniel Asamoah , Basingstoke College of Technology, Basingstoke, England, UK Not Approved VIEWS 0 https://doi.org/10.5256/f1000research.190499.r459009 Strengths: The study aimed to examine responsible and ethical practices in the use of artificial intelligence (AI) among university students, focusing on the influence of affective, cognitive, and behavioural dimensions of AI learning on the development ... Continue reading READ ALL Strengths: The study aimed to examine responsible and ethical practices in the use of artificial intelligence (AI) among university students, focusing on the influence of affective, cognitive, and behavioural dimensions of AI learning on the development of ethical attitudes. The findings revealed that both emotional engagement and technical knowledge significantly shape ethical behaviour, while mere hands-on use of AI tools alone does not guarantee responsible practices. These results emphasise the importance of integrating ethics, critical reflection, and motivation into AI education, suggesting that curricula and pedagogical strategies should go beyond practical training to foster a comprehensive understanding of AI’s societal and ethical implications. The study also highlights the need for institutional policies that promote reflective and responsible AI use and points to future research opportunities using longitudinal designs and broader, more diverse student populations to validate and extend these insights, making it a timely contribution to the evolving discourse on AI literacy and ethical technology adoption in higher education. Suggestions for improvement Abstract I suggest that ‘through’ in the sentence “The analysis was conducted through structural equation modelling to explore the influence of affective….” is changed to ‘using’. Also, the results section can be revised as: The affective and cognitive dimensions exerted a significant and positive impact on the development of learning ethics, whereas the behavioural dimension did not demonstrate statistically significant effects. Furthermore, the study confirmed high reliability and validity of the instrument and revealed a moderate level of AI adoption among students, with tools such as ChatGPT being the most commonly used. Introduction In the sentence “On the one hand, it has been reported that the uncritical…”, did you mean unethical, instead of uncritical? The authors appear to combine the introduction and main literature of the paper, but I would suggest that the introduction is written separately from the literature review. As it stands now, the hypotheses and questions have been written without an analytical and comprehensive background. I would suggest that the introduction is written in about 500 to 600 words to provide the background and context of the study. This section should end with the key aim(s)/objective(s) of the study. With this background, the key research question(s) can also be outlined. After this, a literature review should follow. This should include a conceptual and empirical review that forms the basis of the hypotheses. For example, the concept and empirical work on AI ethics should be highlighted. This should be followed by the concepts and empirical work of affective learning, behavioural learning, and cognitive learning, and how each of them relates to AI or technology-based teaching and learning. Here, point out the gaps in the literature to more clearly reveal what is known and what is unknown in the context of the literature, linking it to the current study. For example, when you state this objective – “Accordingly, the general objective of this study is to analyse responsible practices and ethical behaviours in the use of artificial intelligence among students at the State University of Milagro (UNEMI), with the aim of identifying the predominant factors influencing their ethical development and proposing guidelines for a contextualised and sustainable implementation in higher education environments.” – consider explaining why this study is conducted in this particular context and at this time. Synthesising and analysing the literature and situating it within the context of this study will further clarify the gaps and strengthen the hypothesis development. Methods Justify the use of non-probability sampling and that of convenience sampling. For example, if there were many students from different programmes involved, how were they recruited for this study? How were initial contacts established? Was it not possible to use a probability sampling technique to ensure better representation, or a multi-stage sampling method to account for the wide and diverse populations? Explaining the sampling process of this study more clearly will enhance the credibility and validity of the findings. With respect to the demographic results of the participants, I suggest that the distribution of participants according to their programme of study be included to illustrate the variation in programmes. I also recommend that the rationale for conducting EFA be expanded from merely a reliability check to include an analysis of the factor structure of the questionnaire used. This justification should be supported by relevant literature. Please provide evaluation criteria for the EFA and reliability analysis. For example, if you state that a reliability criterion has been met, what evidence supports this claim? Please include criteria supported by literature for such analyses. Additionally, I suggest including a clear section on the instruments used for data collection. Indicate whether the questionnaire(s) was/were adapted or adopted, and from whom, including details of how the adaptation or adoption was carried out. State the number of items in each section of the questionnaire, including sample items and the rationale for each section. Also, explain how the items are measured, whether on a Likert scale or a forced-choice scale. It would also be helpful to provide the full psychometric properties of the instrument prior to presenting your psychometric reports. I also recommend including a section that describes how the data was analysed and which statistical tools were used. For each research question and hypothesis, specify the key statistical technique employed and the rationale behind its use, supported by relevant literature. For example, if you conducted SEM, explain why this method was chosen for the hypothesis testing, specify whether it was PLS-SEM or Covariance-based SEM, and describe how statistical significance was determined. Since you have stated your research questions, describe how these questions were analysed. These details are crucial for enhancing the credibility of your study. Generally, I will suggest that the method section follows this sequence: Research design and approach Population and sample Data collection instrument Validity and reliability of data collection instrument Ethical considerations Data collection procedures Data analysis Results The authors present that the affective has the strongest relation (β = 0.68); however, the model shows that the beta value for this relationship is 0.39, which indicates a weak positive relationship. The behavioural dimension also exhibits a meaningful relationship with learning ethics (β = –0.05), which is a negative relationship. The cognitive dimension demonstrates a positive association with the ethical construct (β = 0.39), but it is actually 0.52 in the model. This makes the interpretation difficult to follow. I suggest that the model is examined and interpreted carefully, making clear the direction and strength of the relationship, as well as their statistical significance and effect sizes. Once a relationship is established, interpret that relationship; for example, what does a negative effect mean? What does a positive effect mean? Additionally, I am unsure if this study sought to validate the instrument used, as analyses of them have been added to the results section without any basis in the hypotheses or research questions. Issues around convergent and divergent validity, as well as HTMT analysis, are also part of establishing the psychometric properties of the scales used. If you wish to present these as findings, you may want to create a research question or hypothesis for them. Currently, I suggest that you focus on presenting the findings related to the hypotheses more clearly. Furthermore, the research question stated: “What are the responsible practices and ethical behaviours in the use of artificial intelligence among students at the State University of Milagro (UNEMI)?" has not yet been answered. Please address this question in addition to the relationships revealed through the SEM model. Finally, I recommend separating the discussion of results from the presentation of results. There should be a distinct section for the discussion, where you explain why each result was revealed, discuss this in the context of conceptual and empirical literature, add your own insights, and relate the findings to the study context. Currently, the discussion appears to be mixed with the results but lacks in-depth analysis and synthesis to make the implications clearer. Conclusion and Implications for Policy and Practice This section can be revised as "Conclusion and Implications for Policy and Practice." The study can be concluded by summarising the aims and key findings. As I find this study on AI ethics very important, I suggest that implications be carefully considered in relation to pedagogical, policy, and institutional contexts, including AI literacy programmes, future research, student development, and AI implementation in universities. What do your results mean in these areas? What practical measures should be considered to promote ethics? Additionally, a limitations section should be included for this study, particularly regarding the use of non-probability sampling, which affects representativeness. Other limitations should also be acknowledged, and directions for future research should be suggested. Other comments: It would be beneficial to proofread the text after completing all reviews and to check all referencing requirements. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? Partly Competing Interests: No competing interests were disclosed. Reviewer Expertise: Educational Assessment, Large Scale Assessment, Assessment Literacy, AI in Education, Teaching and Learning, Psychometrics, Educational Measurement I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Asamoah D. Reviewer Report For: Responsible practices and ethical behaviors in the use of artificial intelligence among university students [version 1; peer review: 1 approved with reservations, 1 not approved] . F1000Research 2026, 15 :16 ( https://doi.org/10.5256/f1000research.190499.r459009 ) The direct URL for this report is: https://f1000research.com/articles/15-16/v1#referee-response-459009 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Adiyono A. Reviewer Report For: Responsible practices and ethical behaviors in the use of artificial intelligence among university students [version 1; peer review: 1 approved with reservations, 1 not approved] . F1000Research 2026, 15 :16 ( https://doi.org/10.5256/f1000research.190499.r452505 ) The direct URL for this report is: https://f1000research.com/articles/15-16/v1#referee-response-452505 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 30 Jan 2026 Adiyono Adiyono , Islamic education, Sekolah Tinggi Ilmu Tarbiyah Ibnu Rusyd Tanah Grogot, Indonesia, Kabupaten Paser, Kalimantan Timur, Indonesia Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.190499.r452505 General Comments: This is a very strong manuscript with high potential. Its main strengths lie in: Highly relevant and important topic. Rigorous methodological design, particularly regarding instrument validity and reliability, and appropriate use ... Continue reading READ ALL General Comments: This is a very strong manuscript with high potential. Its main strengths lie in: Highly relevant and important topic. Rigorous methodological design, particularly regarding instrument validity and reliability, and appropriate use of SEM. Comprehensive and in-depth data analysis. A well-connected discussion with current literature. Major Revisions: Add an explicit Theoretical Framework/Hypothesis Development section to strengthen the theoretical foundation of the study. Refine and deepen the Discussion section by including more explicit and specific theoretical and practical implications. Strengthen the Conclusion section to state the main contributions and recommendations more concretely, going beyond a summary of the results. Minor Revisions: Consider refining the Title to increase specificity. Revise the Abstract to address gaps and contributions. Add sample questionnaire items in the Methods section. Ensure a clear separation between Results (reporting) and Discussion (interpretation). Perform a final check for consistency in the References format. MAJOR REVISION is required. With the suggested theoretical and interpretative improvements, this article has a high potential for acceptance in a reputable Scopus Q1 journal in the fields of Education, Educational Technology, or AI Ethics. Its methodological quality meets the highest standards. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly Competing Interests: No competing interests were disclosed. Reviewer Expertise: Educational Management, Educational Technology & AI I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Adiyono A. Reviewer Report For: Responsible practices and ethical behaviors in the use of artificial intelligence among university students [version 1; peer review: 1 approved with reservations, 1 not approved] . F1000Research 2026, 15 :16 ( https://doi.org/10.5256/f1000research.190499.r452505 ) The direct URL for this report is: https://f1000research.com/articles/15-16/v1#referee-response-452505 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 06 Jan 2026 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 2 Version 1 06 Jan 26 read read Adiyono Adiyono , Sekolah Tinggi Ilmu Tarbiyah Ibnu Rusyd Tanah Grogot, Indonesia, Kabupaten Paser, Indonesia Daniel Asamoah , Basingstoke College of Technology, Basingstoke, UK Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 Asamoah D. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 02 Mar 2026 | for Version 1 Daniel Asamoah , Basingstoke College of Technology, Basingstoke, England, UK 0 Views copyright © 2026 Asamoah D. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Not Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Strengths: The study aimed to examine responsible and ethical practices in the use of artificial intelligence (AI) among university students, focusing on the influence of affective, cognitive, and behavioural dimensions of AI learning on the development of ethical attitudes. The findings revealed that both emotional engagement and technical knowledge significantly shape ethical behaviour, while mere hands-on use of AI tools alone does not guarantee responsible practices. These results emphasise the importance of integrating ethics, critical reflection, and motivation into AI education, suggesting that curricula and pedagogical strategies should go beyond practical training to foster a comprehensive understanding of AI’s societal and ethical implications. The study also highlights the need for institutional policies that promote reflective and responsible AI use and points to future research opportunities using longitudinal designs and broader, more diverse student populations to validate and extend these insights, making it a timely contribution to the evolving discourse on AI literacy and ethical technology adoption in higher education. Suggestions for improvement Abstract I suggest that ‘through’ in the sentence “The analysis was conducted through structural equation modelling to explore the influence of affective….” is changed to ‘using’. Also, the results section can be revised as: The affective and cognitive dimensions exerted a significant and positive impact on the development of learning ethics, whereas the behavioural dimension did not demonstrate statistically significant effects. Furthermore, the study confirmed high reliability and validity of the instrument and revealed a moderate level of AI adoption among students, with tools such as ChatGPT being the most commonly used. Introduction In the sentence “On the one hand, it has been reported that the uncritical…”, did you mean unethical, instead of uncritical? The authors appear to combine the introduction and main literature of the paper, but I would suggest that the introduction is written separately from the literature review. As it stands now, the hypotheses and questions have been written without an analytical and comprehensive background. I would suggest that the introduction is written in about 500 to 600 words to provide the background and context of the study. This section should end with the key aim(s)/objective(s) of the study. With this background, the key research question(s) can also be outlined. After this, a literature review should follow. This should include a conceptual and empirical review that forms the basis of the hypotheses. For example, the concept and empirical work on AI ethics should be highlighted. This should be followed by the concepts and empirical work of affective learning, behavioural learning, and cognitive learning, and how each of them relates to AI or technology-based teaching and learning. Here, point out the gaps in the literature to more clearly reveal what is known and what is unknown in the context of the literature, linking it to the current study. For example, when you state this objective – “Accordingly, the general objective of this study is to analyse responsible practices and ethical behaviours in the use of artificial intelligence among students at the State University of Milagro (UNEMI), with the aim of identifying the predominant factors influencing their ethical development and proposing guidelines for a contextualised and sustainable implementation in higher education environments.” – consider explaining why this study is conducted in this particular context and at this time. Synthesising and analysing the literature and situating it within the context of this study will further clarify the gaps and strengthen the hypothesis development. Methods Justify the use of non-probability sampling and that of convenience sampling. For example, if there were many students from different programmes involved, how were they recruited for this study? How were initial contacts established? Was it not possible to use a probability sampling technique to ensure better representation, or a multi-stage sampling method to account for the wide and diverse populations? Explaining the sampling process of this study more clearly will enhance the credibility and validity of the findings. With respect to the demographic results of the participants, I suggest that the distribution of participants according to their programme of study be included to illustrate the variation in programmes. I also recommend that the rationale for conducting EFA be expanded from merely a reliability check to include an analysis of the factor structure of the questionnaire used. This justification should be supported by relevant literature. Please provide evaluation criteria for the EFA and reliability analysis. For example, if you state that a reliability criterion has been met, what evidence supports this claim? Please include criteria supported by literature for such analyses. Additionally, I suggest including a clear section on the instruments used for data collection. Indicate whether the questionnaire(s) was/were adapted or adopted, and from whom, including details of how the adaptation or adoption was carried out. State the number of items in each section of the questionnaire, including sample items and the rationale for each section. Also, explain how the items are measured, whether on a Likert scale or a forced-choice scale. It would also be helpful to provide the full psychometric properties of the instrument prior to presenting your psychometric reports. I also recommend including a section that describes how the data was analysed and which statistical tools were used. For each research question and hypothesis, specify the key statistical technique employed and the rationale behind its use, supported by relevant literature. For example, if you conducted SEM, explain why this method was chosen for the hypothesis testing, specify whether it was PLS-SEM or Covariance-based SEM, and describe how statistical significance was determined. Since you have stated your research questions, describe how these questions were analysed. These details are crucial for enhancing the credibility of your study. Generally, I will suggest that the method section follows this sequence: Research design and approach Population and sample Data collection instrument Validity and reliability of data collection instrument Ethical considerations Data collection procedures Data analysis Results The authors present that the affective has the strongest relation (β = 0.68); however, the model shows that the beta value for this relationship is 0.39, which indicates a weak positive relationship. The behavioural dimension also exhibits a meaningful relationship with learning ethics (β = –0.05), which is a negative relationship. The cognitive dimension demonstrates a positive association with the ethical construct (β = 0.39), but it is actually 0.52 in the model. This makes the interpretation difficult to follow. I suggest that the model is examined and interpreted carefully, making clear the direction and strength of the relationship, as well as their statistical significance and effect sizes. Once a relationship is established, interpret that relationship; for example, what does a negative effect mean? What does a positive effect mean? Additionally, I am unsure if this study sought to validate the instrument used, as analyses of them have been added to the results section without any basis in the hypotheses or research questions. Issues around convergent and divergent validity, as well as HTMT analysis, are also part of establishing the psychometric properties of the scales used. If you wish to present these as findings, you may want to create a research question or hypothesis for them. Currently, I suggest that you focus on presenting the findings related to the hypotheses more clearly. Furthermore, the research question stated: “What are the responsible practices and ethical behaviours in the use of artificial intelligence among students at the State University of Milagro (UNEMI)?" has not yet been answered. Please address this question in addition to the relationships revealed through the SEM model. Finally, I recommend separating the discussion of results from the presentation of results. There should be a distinct section for the discussion, where you explain why each result was revealed, discuss this in the context of conceptual and empirical literature, add your own insights, and relate the findings to the study context. Currently, the discussion appears to be mixed with the results but lacks in-depth analysis and synthesis to make the implications clearer. Conclusion and Implications for Policy and Practice This section can be revised as "Conclusion and Implications for Policy and Practice." The study can be concluded by summarising the aims and key findings. As I find this study on AI ethics very important, I suggest that implications be carefully considered in relation to pedagogical, policy, and institutional contexts, including AI literacy programmes, future research, student development, and AI implementation in universities. What do your results mean in these areas? What practical measures should be considered to promote ethics? Additionally, a limitations section should be included for this study, particularly regarding the use of non-probability sampling, which affects representativeness. Other limitations should also be acknowledged, and directions for future research should be suggested. Other comments: It would be beneficial to proofread the text after completing all reviews and to check all referencing requirements. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? Partly Competing Interests No competing interests were disclosed. Reviewer Expertise Educational Assessment, Large Scale Assessment, Assessment Literacy, AI in Education, Teaching and Learning, Psychometrics, Educational Measurement I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above. reply Respond to this report Responses (0) Asamoah D. Peer Review Report For: Responsible practices and ethical behaviors in the use of artificial intelligence among university students [version 1; peer review: 1 approved with reservations, 1 not approved] . F1000Research 2026, 15 :16 ( https://doi.org/10.5256/f1000research.190499.r459009) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/15-16/v1#referee-response-459009 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 Adiyono A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 30 Jan 2026 | for Version 1 Adiyono Adiyono , Islamic education, Sekolah Tinggi Ilmu Tarbiyah Ibnu Rusyd Tanah Grogot, Indonesia, Kabupaten Paser, Kalimantan Timur, Indonesia 0 Views copyright © 2026 Adiyono A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions General Comments: This is a very strong manuscript with high potential. Its main strengths lie in: Highly relevant and important topic. Rigorous methodological design, particularly regarding instrument validity and reliability, and appropriate use of SEM. Comprehensive and in-depth data analysis. A well-connected discussion with current literature. Major Revisions: Add an explicit Theoretical Framework/Hypothesis Development section to strengthen the theoretical foundation of the study. Refine and deepen the Discussion section by including more explicit and specific theoretical and practical implications. Strengthen the Conclusion section to state the main contributions and recommendations more concretely, going beyond a summary of the results. Minor Revisions: Consider refining the Title to increase specificity. Revise the Abstract to address gaps and contributions. Add sample questionnaire items in the Methods section. Ensure a clear separation between Results (reporting) and Discussion (interpretation). Perform a final check for consistency in the References format. MAJOR REVISION is required. With the suggested theoretical and interpretative improvements, this article has a high potential for acceptance in a reputable Scopus Q1 journal in the fields of Education, Educational Technology, or AI Ethics. Its methodological quality meets the highest standards. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly Competing Interests No competing interests were disclosed. Reviewer Expertise Educational Management, Educational Technology & AI I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (0) Adiyono A. Peer Review Report For: Responsible practices and ethical behaviors in the use of artificial intelligence among university students [version 1; peer review: 1 approved with reservations, 1 not approved] . F1000Research 2026, 15 :16 ( https://doi.org/10.5256/f1000research.190499.r452505) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/15-16/v1#referee-response-452505 Alongside their report, reviewers assign a status to the article: Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions Adjust parameters to alter display View on desktop for interactive features Includes Interactive Elements View on desktop for interactive features Competing Interests Policy Provide sufficient details of any financial or non-financial competing interests to enable users to assess whether your comments might lead a reasonable person to question your impartiality. Consider the following examples, but note that this is not an exhaustive list: Examples of 'Non-Financial Competing Interests' Within the past 4 years, you have held joint grants, published or collaborated with any of the authors of the selected paper. You have a close personal relationship (e.g. parent, spouse, sibling, or domestic partner) with any of the authors. You are a close professional associate of any of the authors (e.g. scientific mentor, recent student). You work at the same institute as any of the authors. You hope/expect to benefit (e.g. favour or employment) as a result of your submission. You are an Editor for the journal in which the article is published. Examples of 'Financial Competing Interests' You expect to receive, or in the past 4 years have received, any of the following from any commercial organisation that may gain financially from your submission: a salary, fees, funding, reimbursements. You expect to receive, or in the past 4 years have received, shared grant support or other funding with any of the authors. You hold, or are currently applying for, any patents or significant stocks/shares relating to the subject matter of the paper you are commenting on. Stay Updated Sign up for content alerts and receive a weekly or monthly email with all newly published articles Register with F1000Research Already registered? Sign in Not now, thanks close PLEASE NOTE If you are an AUTHOR of this article, please check that you signed in with the account associated with this article otherwise we cannot automatically identify your role as an author and your comment will be labelled as a “User Comment”. If you are a REVIEWER of this article, please check that you have signed in with the account associated with this article and then go to your account to submit your report, please do not post your review here. If you do not have access to your original account, please contact us . All commenters must hold a formal affiliation as per our Policies . The information that you give us will be displayed next to your comment. User comments must be in English, comprehensible and relevant to the article under discussion. We reserve the right to remove any comments that we consider to be inappropriate, offensive or otherwise in breach of the User Comment Terms and Conditions . Commenters must not use a comment for personal attacks. When criticisms of the article are based on unpublished data, the data should be made available. I accept the User Comment Terms and Conditions Please confirm that you accept the User Comment Terms and Conditions. Affiliation ✕ refresh Please enter your institution. Note: To add your institution or organisation, start typing the name and then select the correct name from the list. Where applicable, the name will appear in both the original language and in English. Do not paste in the name. If the name does not appear in the drop-down list, we will display the information you have entered. ✕ refresh Country/Region * USA UK Canada China France Germany Afghanistan Aland Islands Albania Algeria American Samoa Andorra Angola Anguilla Antarctica Antigua and Barbuda Argentina Armenia Aruba Australia Austria Azerbaijan Bahamas Bahrain Bangladesh Barbados Belarus Belgium Belize Benin Bermuda Bhutan Bolivia Bosnia and Herzegovina Botswana Bouvet Island Brazil British Indian Ocean Territory British Virgin Islands Brunei Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Cape Verde Cayman Islands Central African Republic Chad Chile China Christmas Island Cocos (Keeling) Islands Colombia Comoros Congo Cook Islands Costa Rica Cote d'Ivoire Croatia Cuba Cyprus Czech Republic Democratic Republic of the Congo Denmark Djibouti Dominica Dominican Republic Ecuador Egypt El Salvador Equatorial Guinea Eritrea Estonia Ethiopia Falkland Islands Faroe Islands Federated States of Micronesia Fiji Finland France French Guiana French Polynesia French Southern Territories Gabon Georgia Germany Ghana Gibraltar Greece Greenland Grenada Guadeloupe Guam Guatemala Guernsey Guinea Guinea-Bissau Guyana Haiti Heard Island and Mcdonald Islands Holy See (Vatican City State) Honduras Hong Kong Hungary Iceland India Indonesia Iran Iraq Ireland Israel Italy Jamaica Japan Jersey Jordan Kazakhstan Kenya Kiribati Kosovo (Serbia and Montenegro) Kuwait Kyrgyzstan Lao People's Democratic Republic Latvia Lebanon Lesotho Liberia Libya Liechtenstein Lithuania Luxembourg Macao Madagascar Malawi Malaysia Maldives Mali Malta Marshall Islands Martinique Mauritania Mauritius Mayotte Mexico Minor Outlying Islands of the United States Moldova Monaco Mongolia Montenegro Montserrat Morocco Mozambique Myanmar Namibia Nauru Nepal Netherlands Antilles New Caledonia New Zealand Nicaragua Niger Nigeria Niue Norfolk Island North Korea North Macedonia Northern Mariana Islands Norway Oman Pakistan Palau Palestinian Territory Panama Papua New Guinea Paraguay Peru Philippines Pitcairn Poland Portugal Puerto Rico Qatar Reunion Romania Russian Federation Rwanda Saint Helena Saint Kitts and Nevis Saint Lucia Saint Pierre and Miquelon Saint Vincent and the Grenadines Samoa San Marino Sao Tome and Principe Saudi Arabia Senegal Serbia Seychelles Sierra Leone Singapore Slovakia Slovenia Solomon Islands Somalia South Africa South Georgia and the South Sandwich Is South Korea South Sudan Spain Sri Lanka Sudan Suriname Svalbard and Jan Mayen Swaziland Sweden Switzerland Syria Taiwan Tajikistan Tanzania Thailand The Gambia The Netherlands Timor-Leste Togo Tokelau Tonga Trinidad and Tobago Tunisia Turkey Turkmenistan Turks and Caicos Islands Tuvalu UK USA Uganda Ukraine United Arab Emirates United States Virgin Islands Uruguay Uzbekistan Vanuatu Venezuela Vietnam Wallis and Futuna West Bank and Gaza Strip Western Sahara Yemen Zambia Zimbabwe Please select your country/region. You must enter a comment. Competing Interests Please disclose any competing interests that might be construed to influence your judgment of the article's or peer review report's validity or importance. Competing Interests Policy Provide sufficient details of any financial or non-financial competing interests to enable users to assess whether your comments might lead a reasonable person to question your impartiality. Consider the following examples, but note that this is not an exhaustive list: Examples of 'Non-Financial Competing Interests' Within the past 4 years, you have held joint grants, published or collaborated with any of the authors of the selected paper. You have a close personal relationship (e.g. parent, spouse, sibling, or domestic partner) with any of the authors. You are a close professional associate of any of the authors (e.g. scientific mentor, recent student). You work at the same institute as any of the authors. You hope/expect to benefit (e.g. favour or employment) as a result of your submission. You are an Editor for the journal in which the article is published. Examples of 'Financial Competing Interests' You expect to receive, or in the past 4 years have received, any of the following from any commercial organisation that may gain financially from your submission: a salary, fees, funding, reimbursements. You expect to receive, or in the past 4 years have received, shared grant support or other funding with any of the authors. You hold, or are currently applying for, any patents or significant stocks/shares relating to the subject matter of the paper you are commenting on. Please state your competing interests The comment has been saved. An error has occurred. Please try again. Cancel Post var lTitle = "Responsible practices and ethical behaviors...".replace("'", ''); var linkedInUrl = "http://www.linkedin.com/shareArticle?url=https://f1000research.com/articles/15-16/v1" + "&title=" + encodeURIComponent(lTitle) + "&summary=" + encodeURIComponent('Read the article by '); var deliciousUrl = "https://del.icio.us/post?url=https://f1000research.com/articles/15-16/v1&title=" + encodeURIComponent(lTitle); var redditUrl = "http://reddit.com/submit?url=https://f1000research.com/articles/15-16/v1" + "&title=" + encodeURIComponent(lTitle); linkedInUrl += encodeURIComponent('Echeverria-Caicedo K et al.'); var offsetTop = /chrome/i.test( navigator.userAgent ) ? 4 : -10; var addthis_config = { ui_offset_top: offsetTop, services_compact : "facebook,twitter,www.linkedin.com,www.mendeley.com,reddit.com", services_expanded : "facebook,twitter,www.linkedin.com,www.mendeley.com,reddit.com", services_custom : [ { name: "LinkedIn", url: linkedInUrl, icon:"/img/icon/at_linkedin.svg" }, { name: "Mendeley", url: "http://www.mendeley.com/import/?url=https://f1000research.com/articles/15-16/v1/mendeley", icon:"/img/icon/at_mendeley.svg" }, { name: "Reddit", url: redditUrl, icon:"/img/icon/at_reddit.svg" }, ] }; var addthis_share = { url: "https://f1000research.com/articles/15-16", templates : { twitter : "Responsible practices and ethical behaviors in the use of artificial.... Echeverria-Caicedo K et al., published by " + "@F1000Research" + ", https://f1000research.com/articles/15-16/v1" } }; if (typeof(addthis) != "undefined"){ addthis.addEventListener('addthis.ready', checkCount); addthis.addEventListener('addthis.menu.share', checkCount); } $(".f1r-shares-twitter").attr("href", "https://twitter.com/intent/tweet?text=" + addthis_share.templates.twitter); $(".f1r-shares-facebook").attr("href", "https://www.facebook.com/sharer/sharer.php?u=" + addthis_share.url); $(".f1r-shares-linkedin").attr("href", addthis_config.services_custom[0].url); $(".f1r-shares-reddit").attr("href", addthis_config.services_custom[2].url); $(".f1r-shares-mendelay").attr("href", addthis_config.services_custom[1].url); function checkCount(){ setTimeout(function(){ $(".addthis_button_expanded").each(function(){ var count = $(this).text(); if (count !== "" && count != "0") $(this).removeClass("is-hidden"); else $(this).addClass("is-hidden"); }); }, 1000); } close How to cite this report {{reportCitation}} Cancel Copy Citation Details $(function(){R.ui.buttonDropdowns('.dropdown-for-downloads');}); $(function(){R.ui.toolbarDropdowns('.toolbar-dropdown-for-downloads');}); $.get("/articles/acj/172751/190499") new F1000.Clipboard(); new F1000.ThesaurusTermsDisplay("articles", "article", "190499"); $(document).ready(function() { $( "#frame1" ).on('load', function() { var mydiv = $(this).contents().find("div"); var h = mydiv.height(); console.log(h) }); var tooltipLivingFigure = jQuery(".interactive-living-figure-label .icon-more-info"), titleLivingFigure = tooltipLivingFigure.attr("title"); tooltipLivingFigure.simpletip({ fixed: true, position: ["-115", "30"], baseClass: 'small-tooltip', content:titleLivingFigure + " " }); tooltipLivingFigure.removeAttr("title"); $("body").on("click", ".cite-living-figure", function(e) { e.preventDefault(); var ref = $(this).attr("data-ref"); $(this).closest(".living-figure-list-container").find("#" + ref).fadeIn(200); }); $("body").on("click", ".close-cite-living-figure", function(e) { e.preventDefault(); $(this).closest(".popup-window-wrapper").fadeOut(200); }); $(document).on("mouseup", function(e) { var metricsContainer = $(".article-metrics-popover-wrapper"); if (!metricsContainer.is(e.target) && metricsContainer.has(e.target).length === 0) { $(".article-metrics-close-button").click(); } }); var articleId = $('#articleId').val(); if($("#main-article-count-box").attachArticleMetrics) { $("#main-article-count-box").attachArticleMetrics(articleId, { articleMetricsView: true }); } }); var figshareWidget = $(".new_figshare_widget"); if (figshareWidget.length > 0) { window.figshare.load("f1000", function(Widget) { // Select a tag/tags defined in your page. In this tag we will place the widget. _.map(figshareWidget, function(el){ var widget = new Widget({ articleId: $(el).attr("figshare_articleId") //height:300 // this is the height of the viewer part. [Default: 550] }); widget.initialize(); // initialize the widget widget.mount(el); // mount it in a tag that's on your page // this will save the widget on the global scope for later use from // your JS scripts. This line is optional. //window.widget = widget; }); }); } close Error Close Add Reset F1000.MICROSERVICES.AFFILIATION = ''; $(document).ready(function () { $('.js-affiliations-form').each((index, form) => { new AffiliationForm({ formId: form.id, institutionErrorSelector: '.comment-enter-institution', departmentErrorSelector: '.comment-enter-department', placeSelector: '.js-add-comment-place', stateSelector: '.js-add-comment-state', zipCodeSelector: '.js-add-comment-zipcode', countrySelector: '.js-add-comment-country', countryErrorSelector: '.comment-enter-country', }); }); }); $(document).ready(function () { var reportIds = { "459012": 0, "459011": 0, "459010": 0, "459009": 9, "459008": 0, "450710": 0, "452503": 0, "450711": 0, "450708": 0, "450709": 0, "450706": 0, "450707": 0, "452510": 0, "452511": 0, "452508": 0, "452509": 0, "452506": 0, "450714": 0, "452507": 0, "450715": 0, "452504": 0, "450712": 0, "452505": 14, "450713": 0, "452512": 0, "456542": 0, "456543": 0, "456541": 0, "456550": 0, "456548": 0, "456549": 0, "456546": 0, "456547": 0, "456544": 0, "456545": 0, "448750": 0, "448751": 0, "448748": 0, "448749": 0, "448746": 0, "448747": 0, "448754": 0, "448755": 0, "448752": 0, "448753": 0, "459007": 0, "459006": 0, "459005": 0, "459004": 0, "459003": 0, }; $(".referee-response-container,.js-referee-report").each(function(index, el) { var reportId = $(el).attr("data-reportid"), reportCount = reportIds[reportId] || 0; $(el).find(".comments-count-container,.js-referee-report-views").html(reportCount); }); var uuidInput = $("#article_uuid"), oldUUId = uuidInput.val(), newUUId = "50f6e236-f16b-4e00-ad1d-a3fa6b22664b"; uuidInput.val(newUUId); $("a[href*='article_uuid=']").each(function(index, el) { var newHref = $(el).attr("href").replace(oldUUId, newUUId); $(el).attr("href", newHref); }); }); An innovative open access publishing platform offering rapid publication and open peer review, whilst supporting data deposition and sharing. Browse Gateways Collections How it Works Contact For Developers Cookie Notice Privacy Notice RSS Submit Your Research Follow us © 2012-2026 F1000 Research Ltd. ISSN 2046-1402 | Legal | Partner of Research4Life • CrossRef • ORCID • FAIRSharing R.templateTests.simpleTemplate = R.template(' $text $text $text $text $text '); R.templateTests.runTests(); var F1000platform = new F1000.Platform({ name: "f1000research", displayName: "F1000Research", hostName: "f1000research.com", id: "1", editorialEmail: "[email protected]", infoEmail: "[email protected]", usePmcStats: true }); $(function(){R.ui.dropdowns('.dropdown-for-authors, .dropdown-for-about, .dropdown-for-myresearch');}); // $(function(){R.ui.dropdowns('.dropdown-for-referees');}); $(document).ready(function () { if ($(".cookie-warning").is(":visible")) { $(".sticky").css("margin-bottom", "35px"); $(".devices").addClass("devices-and-cookie-warning"); } $(".cookie-warning .close-button").click(function (e) { $(".devices").removeClass("devices-and-cookie-warning"); $(".sticky").css("margin-bottom", "0"); }); $("#tweeter-feed .tweet-message").each(function (i, message) { var self = $(message); self.html(linkify(self.html())); }); $(".partner").on("mouseenter mouseleave", function() { $(this).find(".gray-scale, .colour").toggleClass("is-hidden"); }); }); Sign In Remember me Forgotten your password? Sign In Cancel Email or password not correct. Please try again Please wait... $(function(){ // Note: All the setup needs to run against a name attribute and *not* the id due the clonish // nature of facebox... $("a[id=googleSignInButton]").click(function(event){ event.preventDefault(); $("input[id=oAuthSystem]").val("GOOGLE"); $("form[id=oAuthForm]").submit(); }); $("a[id=facebookSignInButton]").click(function(event){ event.preventDefault(); $("input[id=oAuthSystem]").val("FACEBOOK"); $("form[id=oAuthForm]").submit(); }); $("a[id=orcidSignInButton]").click(function(event){ event.preventDefault(); $("input[id=oAuthSystem]").val("ORCID"); $("form[id=oAuthForm]").submit(); }); }); If you've forgotten your password, please enter your email address below and we'll send you instructions on how to reset your password. The email address should be the one you originally registered with F1000. Email address not valid, please try again You registered with F1000 via Google, so we cannot reset your password. To sign in, please click here . If you still need help with your Google account password, please click here . You registered with F1000 via Facebook, so we cannot reset your password. To sign in, please click here . If you still need help with your Facebook account password, please click here . Code not correct, please try again Reset password Cancel Email us for further assistance. Server error, please try again. If your email address is registered with us, we will email you instructions to reset your password. If you think you should have received this email but it has not arrived, please check your spam filters and/or contact for further assistance. Please wait... Register $(document).ready(function () { signIn.createSignInAsRow($("#sign-in-form-gfb-popup")); $(".target-field").each(function () { var uris = $(this).val().split("/"); if (uris.pop() === "login") { $(this).val(uris.toString().replace(",","/")); } }); });